Name: AGSL 1001 Course Coordinator: Dr. Mark

Name: Christopher Deonarine
Student ID number: 816013062
Course: Soils and the Environment
Course Code: AGSL 1001
Course Coordinator: Dr. Mark Wuddivira
Title: Soil Texture

Introduction
Soil texture refers to the composition of the soil in terms of the amounts of small (clays), medium (silts), and large (sands) size particles. (Ranjan, Nivetha and Nivetha 2014). To add, soil texture can be classified into 4 main categories clay, sand, loam and silt. However, these categories are further broken down into sub-categories such as sandy loam soil, clay loam soil etc. The function of soil texture is important as it influences nutrient availability. To increase the soil nutrients organic matter such as leaves, manures and other organic matter can be added to the soil texture to aid with plant growth. Soil texture determines the rate at which water drains through a saturated soil and also its erodibility. The pH buffering capacity of the soil is also influence by its soil texture. A soil textural triangle is used to determine the type of soil sample there is by matching the increased percentage of the soil. Stokes’ law applies and it states mathematical V=kr2 where K is constant related to the density and viscosity of the water and the acceleration due to gravity. Stokes’ law focuses on the three forces that act on the soil particle buoyancy force, drag force and the gravitational force. From his law it can be rearranged to find the radius of the particles as they settle as well as the percentage of each size. From this data it can be used to identify the soil textural class. (Brady, Neil and Weil 2008). However, we assume that the density of the water, particles and viscosity remains constant but due to their mineralogical and chemical composition the law can be misleading. Two methods that was used to determine the soil texture are hydrometer method and soil feel method. The hydrometer method is one of the simplest and fastest methods however any large soil particles such as sand settles to quickly below the plane affecting the reading on the hydrometer as it relies on buoyancy of the soil particles. On the other hand, soil feel method is also a quick and easy method as no equipment is being used to determine the type of soil, due to this there is low accuracy.

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Methodology

Hydrometer Method
50g of dried air 2 mm sieved soil was placed in a 600 ml beaker.

5% of 50 ml sodium hexametaphosphate (Calgon) was added. This was a dispersant. The Na+ had replaced Ca2+ on the exchange sites. The PO3- anion had precipitated the exchangeable and soluble cations such as Ca2+, which may cause flocculation.
The mixture was shaken until well mixed and it was allowed to stand for 15 minutes, which had allowed time for the hydration of the particles and the slaked of aggregates.
The contents of the flask were transferred to a dispersing cup of an electric mixer and it was ensured that all the suspension was transferred and that the cup was not too filled.

The mixer was attached to the cup and it was stirred for 2 minutes.

The soil was transferred from the dispersing cup to a 1-L graduated cylinder. The content in the cylinder was bought to the 1000 ml mark in which distilled water was used. The original concentration (g/L) of the soil particles in the suspension was established.

The soil was mixed with a plunger until a uniform suspension is obtained. The plunger was gently removed and the time was noted immediately. The plunger was inserted gently into the suspension in which it was never raised above the surface while it was being mixed, to avoid loss of sample.
A hydrometer was placed gently into the suspension after the plunger was removed and a reading was taken at the end of 90 seconds. 10 cm was measured by the hydrometer since it was the density of the suspension. All the sand particles were assumed to have fallen below this depth after 90 seconds in which the density of the hydrometer was measured (Rs 90-s) of the silt clay in the suspension at this depth.

The cylinder was kept with its content undisturbed for 90 minutes. At the end of the 90 minutes, it was assumed that silt particles would have settled. The hydrometer was gently inserted and the density (Rs 90-m) of the suspension was recorded. This reading was a measured of the clay separated.
The hydrometer correction factor was determined for the density of the dispersing solution referred to as the Blank (Rb) as the hydrometer was gently inserted into the solution provided for this purpose. The Blank was the cylinder that contained the dispersing solution, made up to the mark with distilled water, without any soil sample. The hydrometer value of the Blank (Rb) was recorded.
After the temperature was measured (T) a temperature correction (RT) was performed where RT= (T oC- 19.5) x 0.3.

The percentage of the various soil separates were calculated and the textural class of the sample were determined by use of the textural triangle and the results were tabulated.
Soil Texture by Feel
A handful of soil was taken up into the palm of your hand.

Water was added and the mixture was kneaded into a ball.
The ball of soil was pressed between your thumb and forefinger to form a ribbon.
If a ribbon was formed the length was taken and recorded.
A pinch of soil was taken to form a texture ball in which water was added.
The soil was rubbed and a muddy puddle was made in your palm. The grittiness was recorded.

Results
TABLE 1 SHOWING THE HYDROMETER READINGS AND TEMPERATURE READINGS FOR THE SOIL SAMPLES 1, 2 AND 3 AFTER 90 SECONDS AND 90 MINUTES.

Soil Sample # Hydrometer Value (g/L) Temperature oC90 seconds 90 minutes 90 seconds 90 minutes
Blank 0 0 24 24
1 8 2 24 23
2 23 12 24 23
3 41 36 24 23
Symbols
Rt- Temperature Reading
RC- Corrected Hydrometer Reading
R-Reading
Rtb- Corrected Reading for the Blank
Temperature Correction for Blank at 90 seconds
Rtb 90-s (g/L) = (T0C – 19.5) x 0.3
Rtb = 0+ (24-19.5) x 0.3
= 1.35 g/L
Temperature Correction for Blank at 90 minutes
Rtb 90-m (g/L) = R + (T0C – 19.5) x 0.3
Rtb = 0 + (24-19.5) x 0.3
= 1.35 g/L
Soil Sample # 1
Temperature correction for soil sample #1 at 90 seconds.

Rts 90-s (g/L) = R + (T0C- 19.5) x 0.3
Rts = 8 + (23-19.5) x 0.3
= 9.05 g/L
Final corrected hydrometer reading for soil sample #1 at 90 seconds.

RCs 90-s (g/L) = Rts 90-s – Rtb 90-s
RCs 90-s (g/L) = 9.05-1.35
=7.7 g/L
Percentage of Sand for soil sample #1
% silt + clay = RCs 90-s (g/L) ÷ 50 (g/L) x 100
% silt + clay = (7.7 g/L ÷ 50 g/L) x 100
= 15.4%
% sand= 100% – (% silt + clay)
% sand = 100%-15.4%
% sand = 15.4 %
Temperature correction for soil sample #1 at 90 minutes.

Rts 90-m (g/L) = R + (T0C-19.5) x 0.3
Rts = 2 + (220C-19.5) x 0.3
= 2.75 g/L
Final corrected hydrometer reading for soil sample #1 at 90 minutes.
RCs 90-m (g/L) = Rts 90-m- Rtb 90-m
RCs 90-m (g/L) = 2.75-1.35
=1.4 g/L
Percentage of clay and silt for soil sample #1
% clay = corrected 90-m (g/L) ÷ dry weight of soil (g) x 100
% clay = RCs 90-m (g/L) ÷ 50 (g/L) x 100
% clay = (1.4÷50) x 100
% clay = 2.8 %
% silt = (% silt + clay) – % clay
% silt = 84%-2.8%
% silt= 81.2%
Soil Sample # 2
Temperature correction for soil sample # 2 at 90 seconds
Rts 90-s (g/L) = R (T0C- 19.5) x 0.3
Rts = 23+ (24-19.5) x 0.3
= 24.35 g/L
Final corrected hydrometer reading for soil sample #2 at 90 seconds
RCs 90-s (g/L) = Rts 90-s – Rtb 90-s
RCs 90-s (g/L) = 24.35-1.35
= 23 g/L
Percentage of sand for soil sample # 2
% silt + clay = RCs 90-s (g/L) ÷50 (g/L) x 100
% silt + clay = (23÷50) x 100
=46%
% sand = 100% – (% silt + clay)
= 100-46
% sand = 54 %
Temperature correction for soil sample #2 at 90 minutes
Rts 90-m (g/L) = R + (T0C- 19.5) x 0.3
Rts = 12+ (22-19.5) x 0.3
=12.75 g/L
Final corrected hydrometer reading for soil sample #2 at 90 minutes
RCs 90-m (g/L) = Rts 90-m – Rtb 90-m
RCs 90-m (g/L) = 12.75-1.35
=11.4 g/L
Percentage of silt and clay for soil #2
% clay = corrected 90-m (g/L) ÷ dry weight of soil (g) x 100
% clay = RCs 90-m (g/L) ÷ 50 (g/L) x 100
% clay = (11.4÷50) x 100
% clay = 22.8 %
% silt = (% silt+ clay)- % clay
% silt = 53.4-22.8
% silt= 30.6 %
Soil Sample # 3
Temperature correction for soil sample # 3 at 90 seconds
Rts 90-s (g/L) = R + (T0C – 19.5) x 0.3
Rts = 41 + (24-19.5) x 0.3
=42.35 g/L
Final corrected hydrometer reading for soil sample #3 at 90 seconds
RCs 90-s (g/L) = Rts 90-s – Rtb 90-s
RCs 90-s (g/L) = 42.35-1.35
= 41 g/L
Percentage of sand for soil sample #3
% silt + clay = RCs 90-s (g/L) ÷50 (g/L) x 100
= (41÷50) x 100
=82%
% sand = 100%- (% silt+ clay)
= 100-82
% sand = 18 %
Temperature correction for soil sample #3 at 90 minutes
Rts 90-m (g/L) = R+ (T0C- 19.5) x 0.3
Rts = 36 + (23-19.5) x 0.3
= 37.05 g/L
Final corrected hydrometer reading for soil sample #3 at 90 minutes
RCs 90-m (g/L) = Rts 90-m-Rtb 90-m
RCs 90-m (g/L) = 41.3-1.35
= 39.95g/L
Percentage of silt + clay for soil sample #3
% clay = RCs 90-m (g/L) ÷50 (g/L) x 100
% clay= (39.95÷50) x 100
= 79.9%
% silt= (% silt+ clay) – % clay
% silt= 17.4-79.9
%silt= -62.5%
TABLE 2 SHOWING THE CORRECTED HYDROMETER READINGS FOR SOIL SAMPLES 1, 2 AND 3 AFTER 90 SECONDS AND 90 MINUTES
Soil Sample Number Corrected hydrometer reading after 90 seconds g/L Corrected hydrometer reading after 90 minutes g/L
1 9.05 2.75
2 24.35 12.75
3 42.35 37.05
TABLE 3 SHOWING THE FINAL CORRECTED HYDROMETER READINGS FOR SOIL SAMPLES 1,2 AND 3 AFTER 90 SECONDS AND 90 MINUTES
Soil Sample Number Final hydrometer reading after 90 seconds g/L Final hydrometer reading after 90 minutes g/L
1 7.7 1.4
2 23 11.4
3 41 39.95

TABLE 4 SHOWING THE PERCENTAGE OF SAND, SILT AND CLAY PRESENT IN THE SOIL SAMPLES 1,2 AND 3 ALONG WITH THE TEXTURE OF EACH SOIL
Soil Sample Number % Sand % Silt % Clay Soil Texture
1 15.4 81.2 2.8 Sand
2 54 30.6 22.8 Sandy loam
3 18 -62.5 79.9 Silt loam
Discussion
From our data gathered above we can observe that there was a general increase in the hydrometer values both in the 90 seconds and 90 minutes category. For the 90 seconds category the readings were 9.05g/L, 24.35g/L and 42.35g/L respectively. This type of increase can signal the number of particles in the soil solution as the hydrometer readings increased. It is also noted in the 90-minute category 2.75g/L, 12.75g/L and 37.05 g/L respectively as the hydrometer readings were also high. The hydrometer readings are based on the weight of the liquid. The final hydrometer readings were noted as an increase 7.7g/L, 23g/L and 41g/L respectively were for the 90 second category and for the 90- minute category 14g/L, 11.4g/L and 39.95 g/L respectively. (table 2) Again, this increase is due to the weight of the soil solution and the number of particles in the soil. Temperature remained stable with 10C increase from 230C to 240C for the 90-minute category. (table 1). This 10C rise can be due to the room temperature being too cold hence affecting the temperature of the soil. Also, the time elapse to measure the temperature of the soil can affect the thermometer readings due to the soil being exposed at room temperature. From table 4 we observed that sand being the largest particle had percentages 15.4%, 54% and 18% from the soil samples respectively. Soil Sample 2 had the most amount of sand being 54% compared to soil sample 1 and 2. Soil sample 1 had the largest amount of silt present 81.2% compared to 30.6% and -62.53% respectively. The silt particles are considered to be immediate between sand and clay particles. Soil sample 3 had the largest amount of clay 79.9% when compared to soil sample 1 and 2 being 2.8% an 22.8% respectively. The clay particles are the smallest particles. Using the soil feel method soil sample 1 formed no ribbon and it falls apart in your hand whereas soil sample 2 felt gritty and formed a ribbon, in addition sample 3 made a ribbon when formed however it broke leaving a smooth feeling. From the soil textural triangle sample 1 is sand soil, sample 2 is sandy loam soil and for sample 3 is silt loam soil.
References
Brady, Nyle C, and Ray R. Weil. 2008.The Nature and Properties of soil. Fourteenth ed. Pearson Prentice Hall
Prakash, Nitishrarjan. “soil texture and its impacts on plant growth”, 2014: 1427628.http:// www. Authorstream.com/presentation/ nitishrarjanprakash-1427628-soil-texture-its-impacts-on-plant-growth.

Tarahaat, “texture and soil properties and plant growth”, 2007. http://www.tarahaat.com/ Soil_Texture.aspx

Name:David school sweetheart. Presenting Problem: David is

Name:David Robert Date: 30 October 2018 DOB: 1969
Age: 49 Start Time: ____________ End Time: _____
Identifying Information:
The client’s name is David Robert who is a 49-year-old male who has been married for 21 years and has two adult children. The client works as a metallurgical engineer in a local steel mill where he has worked for 20 years. His hobbies are reading, playing golf, and watching TV. David lives with his wife who was his high school sweetheart.
Presenting Problem:
David is not anymore interested in going to work but rather states that he would rather spent some days just staying at home. David reports feeling blue and his appetite has also decreased. He also reports losing interest in the things, which he used to like preferring to spend time alone in his bedroom. David also complains of irritability and low energy with experiences of physical pain in his back and neck area. David is resistant to going to see a doctor and believes his mood will eventually improve.

Life Stressors:
David feels that he is not worthy living and has tried to take certain measure to help him in addressing the issues that he is facing but nothing seems to work.

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Substance Use: FORMCHECKBOX Yes FORMCHECKBOX No
Due to the sleeping problems that David is having, he drinks more at night having started the drinking habit when he was younger. However, David has reduced the frequency of his drinking habit to two or three beers per night.
Addictions (i.e., gambling, pornography, video gaming)
None reported
Medical/Mental Health Hx/Hospitalizations:
None reported
Abuse/Trauma:
None reported
Social Relationships:
David does not have the desire to go to work anymore rather prefers just staying at home alone. David’s relationship with his wife is described as typical meaning that they do not spend much time together but only meet during meals and attending family gatherings. David prefers spending time alone in his bedroom.
Family Information:
David has a sister whose name is Lisa who had similar problems where she struggled with depression for over 10 years where she is currently receiving medical assistance from a psychiatrist and a counselor. Some of the symptoms, which Lisa had, include emotional and physical fatigue, low mood, increased weight gain, and sleeping problems. David’s oldest son is concerned about his father and feels that his father needs to go and see the doctor due to his unusual behavior.
Spiritual:
None reported
Suicidal:
None reported
Homicidal:
None reported
Assessment:
________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Initial Diagnosis (DSM):
________________________________________________________________________________________________________________________________________________________________________
____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Initial Treatment Goals:
________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Plan:
____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
Name: _____________________________________________ Date: __________________

Name: black teen from the LGBTQ community?

Name: Rushmali Hadley
Subject: LEH 351
Date: 24th May 2018
Professor: David Fletcher
Assistant Teacher: Tashika McBride
How can we stop the Educational Discrimination of LGBTQ Black Adolescent males?
Many people are guilty of discrimination against LGBTQ adolescent males, whether consciously or unconsciously. LGBTQ adolescents are faced with daily discrimination from society and peers and even family members and school officials. This paper will narrow in on discrimination to mainly focus on Educational Discrimination of LGBTQ black Adolescent males and how we can stop or prevent it. Throughout this paper I hope to capture the attention of parents, school officials and peers,what does discrimination
really mean to an adolescent black teen from the LGBTQ community? In a recent sit-down conversation, I was able to come up with some definitions to which I think will perfectly defines discrimination. One of these definitions may be defined as the unjust or prejudicial treatment of different categories of people or things especially on the grounds of race, age, sex. Another definition is treatment or consideration of making distinction in favor of for against. Students who are from the LGBTQ community are exposed to intimidation and violence. They have been deprived of equal education opportunities in schools. In my open discrimination have been the major causes of absenteeism, school dropout, adverse health consequences and low academic achievement among the LGBTQ youths. Discrimination when left undealt with can have a negative impact on a student’s life. Federal protection address discrimination on the basis federal civil rights laws do not expressly protect students from discrimination the basic of sexual orientation or gender identity the student non-discretions act would explicitly prohibit public K-12 schools from discrimination against any students based on or perceived sexual orientation or gender identity. In addition, SNDA of the actual perceives sexual orientation or gender identity of a person with whom they have associated. The bill would them allowed aggrieved individual to assert a violation of these prohibit in some judicial proceeds in addition the student nondiscrimination act (SNDA) would allow federal authorities to address discrimination made unlawful by bill for too many transgender mages an unlawful by bill for too many transgender students allowed them to use the rest room at school. In an article from the William Institute dated 2000 it states that in New York 3.2 million youths ages 8-18 are from the LGBTQ family. Among older you or 1.6 million youths in grades 9-12 approximately 8% or 1.6 million youths in grades 9-12 are identify as either gay bisexual or lesbian and nearly 2% or 150,000 youths ages 13-17 identify as transgender. Documents have shown that 85% of LGBTQ black adolescent have experience verbal harassment while 58% of them felt unsafe at schools due to their sexual orientation and skin color the other 43% felt unsafe because of their gender identity.
As the American public becomes more aware about about the problems members of the LGBTQ community face, this can aid in the acceptance of LGBTQ in the educational system. LGBTQ educational can be fulfilled in a variety of ways, one way is getting to know the person, it can also involve the media that features LGBTQ people seeing Caitlyn Jenner on tv can help make that change.

Ending educational discrimination can be very challenging but if every member in society comes forward a lot can get done. First thing is to end this by teaching students more about LGBTQ issues and the individuals within the classroom could help them better understand the life of the LGBTQ community. With in that teaching students learns about LGBTQ issues and individuals with in the classroom, this could help them better understand LGBTQ people. The acceptance of LGBTQ issues in school’s curriculum could reduce stereotypes and bias against the LGBTQ population. Interacting with people from different background and varying preferences are attributes people are looking for to see if you can interact well with people of a different background. This has shown to improve one’s educational experience as it creates deeper learning and critical thinking. Bringing LGBTQ content to the curriculum could also offer LGBTQ students who feels the effects of bullying in school a safer pace improved educational experience. Educating a child about LGBTQ issues could foster an environment where anyone can feel safe and improve their academic standard.

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Implementing LGBTQ curriculum could combat the widespread homophobia throughout New York. Ensuring that information on the LGBT community is provided to the pubic during the developing years can address this issue. Talking about LGBTQ group contribution to society as well issues they faced could open peoples mind to LGBTQ issues.

While LGBTQ content in the educational pipelines is beneficial for students the way in which the info was presented is just as important if nit more. Going forward teaches can adapt an anti-boss lean this mean educating the students about the history of heterosexual and encouraging these students to speak out in support of the LGBTQ community. Some states have already begun to include LGBTQ. History in their curricula. IN 2011 for example California passed the fair education act. Each requires schools to teach some portion of LGBTQ history. And the results show that both LGBTQ and non-LGBTQ feel safer in classrooms and schools. Creating opportunities and support ins school can help stop the educational discrimination. Creating program or workshops with local or state own government non-profit to support members of LGBTQ maybe in helping them to pay tuition or lust getting some school supplies. Creating a fair school environment can also help put an end to the discrimination make sure the schools had written policy against discrimination of LGBTQ community. Also make sure there is a strong ant harassment and bullying policy that include knew about identity or expression that students know about and staff enforce. Speaking up in the face of anti-transgender jokes are demeaning joes about feminine when there are no longer transgender or gender. Working together to pass laws in the city and states can be very beneficially the outlaw discursion in tr employment housing public accommodation and education base gender identity.

It appears that the lack of support, protection, and guidance from family also has a major effect on LGBTQ youths. Perhaps, if their families were more supportive, I believe that parents should embrace their children no matter what their sexual preference is., I think that family should be the primary source for seeking support and guidance. When parents reject their gay or lesbian adolescent, I feel that it can possibly set him or her up for failure. This era is the time when adolescents would need their parents’ love and support the most. I also sense that when LGBTQ youths don’t get the love and support that they are looking for from parents, it contributes to their state of depression and suicidal phase. Therefore, parents of LGBTQ youths should take time to reflect on the circumstances before they make the wrong decisions.

One way of showing support would be for the youths’ parents or family to intervene with the school or at least try. Overall the two primary sources I see that have the power and ability to end this discrimination against LGBQT youths are schools and parents. In my opinion, they are the ones who have the greatest influence on LGBTQ youths and they are also the ones who spend most time with them so therefore they can have an idea as how they react to certain thing or what they might be thinking. Parents and teachers also can end or help stop the, educational failure, and suicides of our adolescent black males. Parents and schools need to come together to try and to put a full stop to this discrimination. This is 2018 LGBTQ is no longer a topic that is hidden in the closet. If they do decide to work together they can ease the pain some of these students are facing and help bring equality to all students gathered under the same roof. One method that can be exercised in schools is a homosexual sensitivity training for anti-gay students and school officials and parents of that student. The training would benefit both students and school officials and parents. I think that it would help the school officials manage whatever prejudices they may have against LGBTQ youths and help the student deal with whatever they might have against the teachers. Since anti-gay bullying students are perhaps ignorant to the subject, schools should modify a system where all students can be educated on the subject. It would probably help the students get a better understanding if homosexuality was compared to other subject matters such as culture and religion. Students should be provided with a full view of the subject just like any other. If this method helps only two out of ten anti-gay students cease discrimination against LGBTQ students, I am sure that it will make a difference. An additional scheme that should be established is monthly meetings between school officials and parents to review the progress of measures that are already in place and make additional changes if necessary.

Work cited
“How Are LGBT Youths Affected by Discrimination and What Can Schools Do to Help?” York College / CUNY, 31 Mar. 2017, www.york.cuny.edu/academics/writing-program/the-york-scholar-1/volume-5-fall-2008/how-are-lesbian-gay-bisexual-and-transgender-lgbt-youths-affected-by-discrimination-and.

http://lehman.ezproxy.cuny.edu:2048/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=sih&AN=128646249&site=ehost-liveHunt, Jerome. “Five Ways We Can End Discrimination and Harassment Against Gay and Transgender Youth in Schools.” Generation Progress, genprogress.org/voices/2011/03/01/16401/five-ways-we-can-end-discrimination-and-harassment-against-gay-and-tra/.

“Can Education Reduce Prejudice against LGBT People?” The Century Foundation, 30 Sept. 2016, tcf.org/content/commentary/can-education-reduce-prejudice-lgbt-people/.

Name: analytically. However the basic idea of ??

Name: Kunal Shah CWID: 893253773
Analysis and Design of a High Speed Continuous-time Modulator Using ?? the Assisted Op-amp Technique
Abstract: –
In this paper we investigate the use of single bit quantizer in modulator with aim to obtain 11-bit performance in 15MHZ B.W. Here a single bit Continuous time delta sigma modulator (CTDSM) has several advantages over other ADC. Due to single bit quantizer only sign of loop filter o/p is relevant which enables to use high-speed however efficient & feed forward 2-stage op-amp with limited output swing. However here “assisted op-amp” technique is used i.e. to obtain required linearity with low power consumption. At high-speed timing mismatch effects in assisted integrator, which can be potential problem.
Keywords: – Analog-to-digital converter (ADC), continuous-time circuit, continuous-time integrator, delta-sigma, and continuous time delta sigma modulator
Introduction: –
A CTDSM has three main important component – A loop filter H(z), A clocked quantizer, A feedback digital-to-analog converter (DAC). The quantizer is strongly non-linear circuit in a linear system, which makes the behavior of ?? modulator very complicated to investigate analytically. However the basic idea of ?? modulator is that the analog input signal is modulated into a digital whose spectrum approximates that of the analog input well in a narrow frequency range but which is other wise noisy. The noise arises from quantization of an analog signal and the loop filter shapes the quantization noise away from desired frequency range.

CTDSM were the burden of achieving high open loop gain is distributed among several op-amps. As stated earlier the modulator here depends upon o/p of open loop filter op-amp speed requirement are greatly relaxed.
1. Continuous time delta sigma modulator: –

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Fig 1(a) Basic CTDSM
The basic principle of this ?? modulator is to enclose a simple quantizer in feedback loop to shape quantization noise such that most noise is shifted out of band, which can be later suppressed by filtering. Here in this figure 1(a) the quantizer has been modeled by additive white noise source Ej(k)

Fig 1(b) Basic CTDSM graph
This graph indicates the T.F knows as Noise TF (NTF) from quantization noise Ej to modulator o/p. From this graph it is apparent that the modulator emphasizes the quantization noise at higher frequency. Then this noise is filtered out reducing total in band quantization noise power in modulator o/p. Input impedance of CTDSM is resistive greatly simplifying the design of device. Further with proper design choice of feedback DAC this modulator have implicit anti-aliasing multibit quantizers in the ?? loop are used for high performance conversion. It has several benefits such as more aggressive NTF can be chosen higher in band SQNR for given oversampling ratio (OSR). If NRZ DAC is used, sensitivity of converter to clock jitter is reduced. But it has few drawbacks as well specially at high speed. 4-bit flash ADC designs are straightforward but quantizer needs to provide an o/p within one clock cycle. This makes it in power dissipation of open loop filter.

Modulator architecture: –

Fig 2 Modulator architecture
This figure is of ?? modulator. A single ended diagram is for simplicity and negative value of resistor (-Rx1,-Rx2,-Ra,-Rc) indicate sign inversion of the signals. According to Lee’s rule max flat behavior of out of band gain 4th order NTF should be max of 1.5 sampling rate is 32 implying an average of 32. For enhanced noise shaping complex zeroes are placed in signal band. This results in peak in band SQNR of 77dB, which is 10dB above the desired SNR. However even if 4 bit quantizer is same, SQNR can be achieved but with sampling rate of only 400MHZ. The loop filter is implemented by cascade of integrators with feed forward. It is direct path added from modulator i/p to loop filter o/p due to which it only process quantization noise and results in small value of integrating capacitor. For low noise and results in small value of integrator use active RC design. In 1st integrator the resistor is implemented using assisted op-amp technique. Weighted addition of integrator o/p is performed using various summing amplifier (A5). Capacitive feeds ins to the last integrator which could have accomplish summation but instead of it a dedicated summing amplifier is used to add integrated o/p. In capacitive summation approach, 4th integrator would be in high-speed path of modulator loop and the feed in capacitor needed to implement summation will result in increased delay. Thus this is more power efficient solution. Due to single bit quantizer o/p of summing amplifier can be scaled without efficient NTF. This implies to the design, as o/p of summing op-amp does not depend to accommodate full-scale swing. Feedback resistor and i/p capacitance of A5 occurs at high frequency, which reduces the delay.
There are other ways to implement DAC such as: –
Switched capacitor DAC: – It makes modulator performance robust to clock jitter. It also results in increased in band thermal noise due to aliasing effect. The linearity needed for 1st op-amp is also increased due to spikes of current resulting from discharge of f/b DAC capacitor. Here NRZ DAC (DAC1) was chosen as f/b DAC. A RZ DAC is sensitive to clock jitter. Therefore NRZ can be implemented with switched cap, which reduces open loop gain around 1st op-amp, which increases excess delay. DAC2 compensates for excess loop delay, which is about 55% of clock period. The peak-to-peak voltage of modulator is 2.4V. All the op-amp here is 2-stage NMOS i/p which results in high speed and also feed forward compensation. Due to assisted op-amp the 1st integrator is almost ideal. Finite B.W and excess delay degrade the stability of modulator. To counter RC time constant, resistor and capacitor are digitally tuned.

Assisted Op-amp integrator and timing skew: –

Fig 3 (a) Effect of non-linearity in CTDSM
Here in fig 3.(a) (a) shows the first order of CTDSM with non linear integrator p(t) denotes the pulse shape of feedback DAC. The operational transconductance amplifier (OTA) in integrator is assumed to be weakly non linear with an o/p current given as I = GmVi – G3Vi3. Effects of OTA are as follows: –
– o/p sequence v1k of modulator with open loop filter excited by input u(t) from fig B
– OTA i/p voltage is denoted by x11t
In fig (c) to determine the effect the non linearity on modulator, the non linear current caused by OTA G3 x11(t)3 is injected into linear modulator with i/p nulled and quantizer by passed. The true in band power spectral (PSD) due to non-linearity can be approximated as V1 k + V3k.

2 keys are taken into consideration: –
Nonlinearity can be thought of manifesting from nonlinear current injected into linear (as from figc). Therefore in band distortion can be reduced by reducing internal swing X1(1)(t) or g3 or both.

The PSD of in band noise due to OTA non linearity is related to PSD G3x1(1) (t)3.

FIG 3 (b) Assisted Op-ampThis figure shows the idea behind assisted op-amp applied to active RC integrator. This op-amp is feed forward with phase margin Gmf . Current flowing integrating capacitor is Vin/R ± Iref. Swings at the internal nodes can be avoided by using assisted currents. This way the current through integrating capacitor is absorbed by the assistant and Vx1(t) and Vx2(t) are zero results in improved linearity. If Vx1(t) = 0 i.e the integrator will remain ideal with very less delay.

Fig 3 (c) NTF ModulatorFig 3 (d) SDSR vs. Skew

Fig 3 (e) Timing Skew
Fig 3 (c) shows NTF modulator with both using op-amp assistance and even without it for 1st integrator. Due to op-amp assistance integrator excess delay is reduced. One issue with assisted op-amp integrator is timing skew between feedback ; assistant DAC current causing Vx1(t) and Vx2(t) to be non zero. This current are proportional to V3x1(t) and V3x2(t). Due to non-linearity the components have very little power at low frequency.
From the figure 3 (d) we can say that the curve is asymmetric about ? = 0 and becomes more so with increased non linearity this is due to overlap capacitance in 2nd stage of feed forward amplifier.

Fig 3 (e) shows the positive timing skew is defined as feedback DAC pulse leading to assistant DAC pulse. The figure shows when skew is -0.15Ts for T ; T1 , IDAC is negative which causes Vx1 = -Vmax. At T=T1, Iasst changes its state early which causes current of 2 IDAC to be drawn from o/p node and for T;T2 by virtual ground concept will attempt to settle at +Vmax . For +ve timing skew the operation is different. For T ; T2 Vx1 = -Vmax and for T3 ; T ; T2 net current of 2IDAC is injected into o/p node since DAC is not changed it’s state which causes Vx1 to increase causing +ve. However for T > T3 it will settle at Vmax.

Circuit Design – Operational Amplifier (Op-amp): –

Fig 4 (a) Op-amp
This circuit is a two-stage design that uses feed forward compensation. This architecture is more efficient when compared to miller compensated design, as power is not wasted during charging and discharging of the capacitor. First stage consists of M1, M2, M3 and M4 as long channel device to lower the 1/f component of input referred noise. The output mode of 1st stage is M5 and M6 they are minimum length devices. M9, M10, M5 and M6 have same VDS . As there is a common mode voltage at output stage the DC gain is not degraded by feedback mechanism. Here M5, M6, M8 and M9 are optimized for speed.

Latch, DAC & Assistant Transconductor: –

Fig 4 (b) Latch Fig 4 (c) DAC

Fig 4 (d) Assistant transconductor
Fig (b) consists of CMOS latch, which has a reset phase. Here the latch output is coupled with C2MOS buffer by delayed version of clock. During the reset phase (?3) the output of latches are shorted which causes differential input of latch to become zero and beginning of Q1 which addresses latch hysteresis. C2MOS reduces the data dependent Q1 jitters and assistant path.

Fig (c) consists of schematic diagram of feedback and assistant DAC. Noise from DAC bias circuitry RC filtered so that it does not contribute significant noise. It is implemented with cascade NMOS & PMOS as current source using differential pair. This helps in compensating current flowing through parasitic capacitor at output of 1st integrator.

Fig 4 (d) shows the schematic of assistant transconductor used to inject Vin/R into op-amp output. This circuit is biased so as to have sufficiently large bandwidth. It is class AB design comprising of complementary common gate M2 & M7. M4 & M9 are same size as M3 & M8 respectively.
5. Future work of CTDSM: –
There are of course number of area in which the CTDSM could be well advanced thus improving their usefulness in a wider range of application.
1. Multibit DAC: – A working high speed multibit design could be significant break through not only they have high resolution and more stable but they improve on clock jitter, sensitivity too.

2. Power consumption: – Power can be reduced through non-bipolar circuit or lower supply voltage while maintaining speed.

6. Conclusion: –
The assisted op-amp technology is originally proposed to improve the linearity of low speed high resolution CTDSM was successfully applied to the design of high speed single bit modulator but at high clock rates timing mismatch between the feedback and assistant DAC can be a potential problem.

Name: are acknowledged, and the sources cited are

Name: Ajay Nair
Student ID:17211015
E-mail: [email protected]
Programme: Msc in computing
Module code: MCM
Date of submission: 10-08-2018
Project Title: Smart City Services and Sentiment Analysis
Supervisor: D.Sc. Antti Knutas
Disclaimer:
A report submitted to Dublin City University, School of Computing MCM Practicum, 2017/2018. I understand that the University regards breaches of academic integrity and plagiarism as grave and serious. I have read and understood the DCU Academic Integrity and Plagiarism Policy. I accept the penalties that may be imposed should I engage in practice or practices that breach this policy. I have identified and included the source of all facts, ideas, opinions, viewpoints of others in the assignment references. Direct quotations, paraphrasing, discussion of ideas from books, journal articles, internet sources, module text, or any other source whatsoever are acknowledged, and the sources cited are identified in the assignment references. I declare that this material, which I now submit for assessment, is entirely my own work and has not been taken from the work of others save and to the extent that such work has been cited and acknowledged within the text of my work. By signing this form or by submitting this material online I confirm that this assignment, or any part of it, has not been previously submitted by me or any other person for assessment on this or any other course of study. By signing this form or by submitting material for assessment online I confirm that I have read and understood DCU Academic Integrity and Plagiarism Policy (available at: http://www.dcu.ie/registry/examinations/index.shtml)
Name(s): Ajay Nair
Date: 10-08-2018
Smart City Services and Sentiment Analysis
Ajay S Nair
School of Computing
Dublin City University
Dublin, Ireland
[email protected]
Abstract -: A major part of the population in this world resides in urban areas. Each city with its facilities and services is highly important in every aspect. With everyday progress, all major cities are lagging in one area or another in terms of services, policy making, facilities, and planning. The main objective of this research is to analyze what services people think and expect from city services and using sentiment analysis, in order to find the most satisfactory level of each service. This research accessed the forum discussion dataset which is populating in one of the major discussion forums in Ireland called “Boards.ie” and runs sentiment analysis on the resulting topic models. The major question which leads to this paper is “what do people expect from a city and how can this be achieved through a study on current scenarios?”. The expected findings should showcase areas where improvements are needed and that helps towards better planning and policy-making of city authorities.
Keywords— HTML Data scrapping, LDA Topic Model, Sentimental analysis, City services, text mining
Introduction
Urban areas called smart cities are cities that use operational and feedback data from a variety of sources like power consumption statics, buying power and trend statistic, employment indexes, traffic congestions, public safety events, social media discussions etc. to optimize city services 1 and life quality of people residing there. Over the last decade, the smart city idea is prominent and as per the 2014studies, 2 there are 26 smart cities around the world and more are expected to be coming up noticeably in North America and Europe by 20252. This should be facilitated by detailed analysis, planning, developing and adopting digital systems and technologies, which help to improve the efficiency and quality of life of urban citizens.

The research goal of this study is to identify and filter out sentiments of people living in Dublin regarding all area of city services and filtering out areas that require more focus and improvement with the help of topic modeling and sentiment analysis.
Background and Motivation
Ireland has a fast-growing market with a high-velocity growth in both infrastructure and technology sector. This growth is clearly visible in all parts of Dublin and the city needs more planning and service schemes to cop up with this positive growth.

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This study deals with city services and sentiment of all topics regarding Dublin and aims to filter out all major service sectors that are co-related to Dublin city. There are various studies themed on Smart city services and sentiment analysis, which include various topics like Sentiment Analysis, Gensim-LDA Topic Modeling, Core Smart City and Its Benchmarks etc.

Topic modeling is basically used to identify and cluster out major topics from a set of documents or data. In this study, LDA topic modeling is followed by with the help of Gensim3, which is basically a python library meant to deal with large data performance operations like LDA Topic Modeling, LSI Topic Modeling, TF-IDF Calculations, Tokenization’s etc. in an easy manner.

Sentiment analysis has a number of applications in the modern world like behavior analysis, hate speech detection, crime rate prediction and prevention, satisfactory analysis, social media analysis, e-commerce, digital marketing etc. But in this study, it is limited to smart city services where it deals with what people discuss, debate and expects from a smart city. The most used versions of sentiment analysis have three results or emotions: positive, negative and neutral, however new research papers were introduced in past few years to overcome the limitations of this basic analysis4. In most of the sentiment analysis cases, major content of the sentiment is identified first, while categorization of the sentiment is a more difficult task.

RELATED WORKS
‘The evolution of sentiment analysis'(V.Mäntylä, D. Graziotin and M. Kuutila, 2016 5 paper is highly recommended to learn how and when sentiment analysis started and in which era its growth got boosted. This paper gives a whole idea about what sentiment analysis is, the research area where this is dealt with, and its history. It discusses all possible results of the analysis, trending areas on it, applications of sentiment analysis and human behavior-based goal classification and top citation sites to find research paper’s limitations of the current sentiment analysis. This paper can be a good aid for a beginner in sentiment analysis since this covers all areas of sentiment analysis.

V.Mäntylä, D. Graziotin and M. Kuutila conducted the study on’The evolution of sentiment analysis’ 5 using word sentiment analysis in Google Scholar and corpus database in 2016. They filtered out and made a cluster of articles using LDA topic modeling and did the manual quantitative analysis. They found that since 2005 there is a visible increase in the number of papers published related to sentiment analysis and most of them are related to opinion mining. This paper also shows that there is a simultaneous increase in citation count with a number of papers and it surpasses the count of the much mature and large research area of software engineering. V.Mäntylä, D. Graziotin and M. Kuutila classified wide analysis methods into three categories called machine learning, natural language processing, and sentiment analysis specific method. The notable change found in recent papers is that they are mainly concentrating on social media, such as Twitter, Facebook etc. and indicates the current trend in the market and the technology target.

‘Social data sentiment analysis in smart environment'(Vakali et al., 2013) 6study describes the implementations of sentiment analysis in smart platforms. One can observe from the report that the success of sentiment analysis is purely based on two points: the first one being how to design the processes which should be closely related to human behavior and the second one being how to implement an idea in a computational way.

The authors Vakali, Despoina Chatzakou, Vassiliki Koutsonikol, Georgios Andreadis 6 addresses the challenge to go beyond normal polarities of human behavior and to accommodate more wide and complicated emotional processes in social media opinions. The authors made use of seminal ones in psychology in sentiment analysis and it helped him to categorize human emotions into six types, which help to create a wider spectrum compared to the basic dual polarity. He proposed a spectrum of six emotions: anger, disgust, fear, joy, sadness and, surprise.
The authors used two main parameters for the computational procedure: intensity and valence; which help in semantic with the emotional scaling. They help in discovering the emotional relevance of the tweets and qualifying merits of the emotions. After finding relations between tweets and the six primary emotions, the next step is data analysis through data summary (Example- K mean can be used for grouping tweets with similar emotions). The main advantage of this paper is that it is theoretically and technically sounder and more descriptive compared to most other papers.

In the study of ‘Multi-Aspect Sentiment Analysis with Topic Models’ (Lu et al., 2011 7, the authors concentrate on how to classify and mine out best user ratings and contents using different topic modeling. This paper explains all about different LDA models and the differences between them, including types of labeling, the performance, and efficiency. It describes the way documents are represented as mixtures over latent topics. It also describes multi-grain LDA and segmented topic modeling. In this paper, the author compares a few unsupervised, weakly supervised topic modeling examples and discusses two major multi-aspect sentiment analysis called Multi Aspect Sentence Labeling and Multi Rating Prediction. For both multi-aspect sentiment analysis, the authors used four different kinds of topic modeling called LDA, Local LDA, Multi-Grain LDA and Segment Topic modeling.
Multi-aspect sentence labeling is used here to label and gather out different reviews of restaurants from different regions and further summarization. Multi-aspect rating prediction is to predict implicit aspect specific star ratings for every review. The authors found that weakly supervised topic modeling did well over Multi-aspect sentence labeling and only works well for Multi-aspect prediction with indirect supervision. However, it was found that unsupervised topic modeling gave a high rate of performance only in weak prediction models.

In ‘Large scale data analytics for smart cities and related use cases'(Barnaghi, 20148paper, the author emphasizes on data mining by technical solutions to handle large data and tries to find patterns, co-occurrences, and trends from a large volume of data in the project presentation. The author used examples, 101 smart cities use cases, a lot of visualization etc. to depict the findings. The author also worked on visualizing how data analysis works for smart city development. Six stages were suggested in data analysis for smart city projects: The first stage deals with the collection of data; then data is filtered and preprocessed in the second stage; metadata integration and post-process pattern recognition are considered as third and fourth steps. These patterns should be analyzed semantically and thus results can give a better visualization in the last step. This article covers only basic ideas of data analysis in smart cities. However, better visualization and pictorial representations are the reader’s assets to understand the correlation between different areas.

Liangjie Hong and Brian D. Davison conducted a study on topic Empirical Study of Topic Modeling(Hong and D. Davison, 20109 and in this paper, the authors convey that by training a topic model on aggregated messages, it is possible to increase the quality of learned model which boosts performance significantly in real-world classification problems. The authors used several schemes to train a standard topic model and to find the quality and effectiveness through some experiments. This paper progresses through three stages: The first stage explains some existing LDA topic models. It also points out different extensions of LDA and how this is different from standard text mining tools. In the second phase of the paper, working of LDA and Author Topic modeling is described.LDA has a set of common processes which are applicable to all document collections. For each document, LDA picks a topic from its distribution over topics. It then samples a word from the distribution of words and this process is repeated for all words in the document. Author topic modeling is just an extension of LDA. In this, we need to consider two latent variables an author x, and topic z for each word. The main difference from LDA is that each individual document has an extra observed variable part called ‘Author’. So a combination of authors and words in the document gives the observed variable count for an individual document.

The core part of ‘Empirical Study of Topic Modeling’ 9 describes different training models and training steps. AT, MSG and TERM are the training models used here. Twitter data is used here to perform two main tasks which are predicting popular messages and grouping users on the basis of topical categories. Through experiments it was found that the document length is directly related to the effectiveness of topic model and aggregated short messages; this can produce a better training model. Also, it was observed that extension to AT model does not act as an effective modeling for messages or users and normal LDA is acting better on user aggregated profiles.

Research Plan
The main objective of this research is to achieve a result which is more relevant and accurate about city data which helps future planning and insight driven approaches to develop a smart city project. The research plan is briefly mentioned in the chart below.

Chart 1 – High-level research workflow
Collect Data

Scrap ; Filter Data

LDA Topic Modeling

4 Topic Sentiment Analysis
Modeling
8 Topic Sentiment Analysis
12 Topic Sentiment Analysis

Validation

A. Data collection and creating a database
In this stage, the main aim is to collect raw data, clean it and store it in a form that it is easily available for analysis. In this project, the data set is expected to be in the form of metadata or web-based data, so web data scrapping and parsing are essential in this stage(Refer to Table 1).

Table 1- Information about data source and type of data
Source Data format Data Size
Boards.ie
http://data.sioc-project.org/download
16 Meta Data
XML-RDF-Format Size on Disk -:14.5MB
Sentences Count-3554
Word Count-
129,644
B. Feature extraction and LDA Topic Modeling
Parsing of data is done by using lxml10, 11python library. The row data is arranged in a tree-shaped structure (Refer to Fig. 1.) and the parsing is made use of ‘etree’11module of ‘lxml’ library. The output of scrapped data is stored in .txt format in a text file(Refer Fig 2).

At this stage, the filtered data from the discussion forum is clustered (Topic Modeling) out using Gensim 3 topic modeling algorithms. A bag of words called Key-list was also used to filter out data which is only related to Dublin. Gensim uses ‘Numpy’and ‘Scipy’ 12 for performance. It is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiate it from most other scientific software packages. LDA13,14,15 is used internally because LDA is a part of Gensim and it will help to discover a semantic structure (Meaningful insides which help in better decision making) of the documents by analyzing the SIOC corpus16

Fig. 1 Example of data-Meta Data format

Fig. 2 Output of html parsed data
C. LDA Visualization
py-LDA-vis 17is a python library which is known as the best library for visualization of topic modeling. This study aims to make utilize py-LDA-vis 18 for visualization assuming it helps to improve the understandability of topic modeling relevant to this study(Refer to fig 3).

D. Sentiment Analysis
The most interesting area of this research is sentiment analysis, and this is done with the help of ‘Thematic coding’ (Topic Clustering which is done on the previous step). Thematic coding helps to group data on the basis of the themes, which then undergoes sentiment analysis to find out which emotion suits the data better. Sentiment analysis methods chosen here are VADER Sentiment, AFINN and TextBlob Sentiment analysis.

VADER 19 (Valence Aware Dictionary and sEntiment Reasoner) is a fully open sourced lexicon and rule-based sentiment analysis tool specially designed for social media expressions.

AFINN 21 is a wordlist-based approach for sentiment analysis. AFINN is a list of English words rated for valence between -5 and +5 which is manually labeled by Finn Årup Nielsen in 2009-2011.

TextBlob 20 is a python library used to process textual data and delivers common natural language processing (NLP) tasks like sentiment analysis, noun phrase extraction, classification etc.

Different types of sentiment analysis 22help to improve the accuracy and precision of the findings. Cross-validation is planned to make use of positive, negative and overall accuracy score of VADER and TextBlob sentiments. However, all the LDA topic modeling output may not consider the final analysis. It is based on inclusion and exclusion criteria which are used to identify topics which only contain words similar to each other. If a topic is negative (The majority of sentiments among 3 methods) and contains 50% or more words, where words are not related to the similar area, then that topic must be excluded from the study and it is planned to do manually.

E. Evaluation
The major challenge in this study is the evaluation of results. Plans to do cross-check operation with a manually labeled dataset and expecting accuracy range between 70-80% considering high volume of data. Positive, negative and overall accuracy score of VADER and TextBlob sentiments also deliver a secondary validation 23 on this research.

Results
Fig. 3. py-LDA-vis -LDA visualization, 4 Topics are considered
-2946405684520LDA Topic modeling results reveal the most relevant and important topics which appear in discussion forums. There is an option to finalize the number of topics to get as LDA 15 output. Initially, four topics were considered which were more relevant to Dublin and appeared in the discussion forum.

The output of the LDA topic modeling is shown in below example
(0, ‘0.027*”one” + 0.009*”great” + 0.009*”many” + 0.009*”going” + 0.008*”last” + 0.008*”still” + 0.008*”ie” + 0.007*”thing” + 0.007*”bit” + 0.007*”two”‘)
The output contains the topic number, the most prominent words and its probability distribution in that particular topic. In the example above, the first 0 indicates the topic number and 0.027 indicates the probability (27 %) of the word ‘one’ in topic 0.

Py-LDA-vis 18 is a python library which is mainly used for visualization of LDA topic modeling results in an easier and user interacting manner. The main four topics of the above example are listed out by LDA visualization and shown in Fig 3. The same process is repeated for 8 topics and 12 topics and the results are visualized using the same method shown above.

In the next stage, run an algorithm to find the topic number of each word in the corpus. Refer Table 2, where corpus word (1, 1) has topic id 0 and the probability of that word is in topic 0 is 62.47%. This experiment is repeated for a different number of topics, i.e. it is repeated for 4 (Output – shown in below Table 2), 8 and 12 topic LDA models
Table 2- Corpus ; corresponding Topic Number table
Corpus Probability
(*100 = %) Topic No
(1, 1) 0.6247 1
(2, 1) 0.6247 0
(3, 1) 0.6245
1
(4, 1)
0.6247
0
(5, 1)
0.6244
1
(6, 1)
0.6244
0
(7, 1)
0.6249
2
In the next step, sentiment results are stored
in a csv file. Three different observations are listed out
for each sentence of input, i.e. sentiment scores of
VADER 20 Sentiment analysis, AFINN 21 sentiment
analysis, and TextBlob 20 Sentiment analysis outputs
are stored in an output csv file. This experiment is also
repeated for a different number of topics, i.e. it is
repeated for 4 (Output – shown in below Table 3), 8
and 12 topic LDA models. The output file contains each
sentence, sentence number, topic number indicating the
corresponding topic of each sentence and sentiments
scores, and this output is considered as the input for the
next stage of the result. (Refer Table 3)
Table 3- Sentence -Corresponding topic and sentiment of the sentence
Topic Number VADER Sentiment AFINN-Sen TB- Sentiment TB- Subjectivity
Topic0
{‘neg’: 0.0, ‘neu’: 1.0, ‘pos’: 0.0, ‘compound’: 0.0}
0
0 0
neutral
{‘neg’: 0.0, ‘neu’: 0.0, ‘pos’: 0.0, ‘compound’: 0.0}
0 0 0
Topic7
{‘neg’: 0.293, ‘neu’: 0.707, ‘pos’: 0.0, ‘compound’: -0.6597}
-5
0.1
0.2
neutral
{‘neg’: 0.0, ‘neu’: 0.0, ‘pos’: 0.0, ‘compound’: 0.0}
0 0 0
In next stage of research, an algorithm was run to cluster out sentiments of each topic (Refer Fig. 4.) and to find the overall sentiment scores of each topic as per the three different methods chosen earlier.
The output of each topic is mentioned in the tables below.

A. LDA with 4 topics
Table 4- 4 topics and VADER,TextBlob and AFINN sentiments
Topic VADER Sentiment Result AFINN Sentiment Results TextBlob Sentiment Result
Topic1 Pos Pos Pos
Topic 2 Pos Neg Pos
Topic 3 Pos Pos Pos
Topic 4 Neg Neg Neg
B. LDA with 8 Topics
Table 5- 8 topics and VADER, TextBlob and AFINN sentiments
Topic VADER Sentiment Result AFINN Sentiment Results TextBlob Sentiment Result
Topic 1 Pos Pos Pos
Topic 2 Pos Pos Pos
Topic 3 Pos Pos Pos
Topic 4 Neg Neg Neg
Topic 5 Pos Neg Pos
Topic 6 Pos Pos Pos
Topic 7 Pos Pos Pos
Topic 8 Pos Pos Pos
C. LDA with 12 Topics
Table 6 – 12 topics and VADER, TextBlob and AFINN sentiments
Topic VADER Sentiment Result AFINN Sentiment Results TextBlob Sentiment Result
Topic 1 Neg Neg Neg
Topic 2 Pos Pos Pos
Topic 3 Pos Pos Pos
Topic 4 Pos Pos Pos
Topic 5 Pos Pos Pos
Topic 6 Pos Pos Pos
Topic 7 Pos Pos Pos
Topic 8 Neg Neg Pos
Topic 9 Pos Pos Pos
Topic 10 Pos Neg Pos
Topic 11 Pos Pos Pos
Topic 12 Pos Pos Pos
D. Validation results
Validation is done by two streams: one is through accuracy comparison on the sentiment prediction of VADER and TextBlob methods. It can refer as a validation on non-labeled data set.(Table 7 and Table 8)
D.1 Accuracy score of VADER Sentiment analysis
Table 7: Accuracy score table of a different number of LDA Topic model outputs using VADER sentiment.

LDA Topic Model Accuracy Score
4-Topic model 76.27
8-Topic model 51.38
12-Topic model 51.41
D.2 Accuracy score of TextBlob Sentiment analysis
Table 8: Accuracy score table of a different number of LDA Topic model outputs using TextBlob Sentiment
LDA Topic Model Overall Accuracy Score
4-Topic model 24.55
8-Topic model 24.08
12-Topic model 24.35
Fig. 4. – Sentence distribution probability on each topic

In the second stream of validation, sentiment prediction accuracy was observed from a manually labeled data set. Every sentence in the data set was manually tagged as positive, negative or neutral as per the nature of the sentence. The accuracy scores of each method are shown in the table below (Table 9).

Table 9: Accuracy score, Precision, F-score, recall table on manually labeled data.

Method Accuracy Precision Recall F-Score
VADER Sentiment 82.81 NA NA NA
TextBlob Sentiment 50.75 NA NA NA
Logistic
Regression 22 53.13 55.63 55.16 52.71
SVM model 22
46.88 47.98 48.02 46.82
NAIVE BAYES MODEL
22
50.00 48.33 48.41 48.18
RANDOM FOREST MODEL 22
62.50 70.83 65.87 61.13
DECISION TREE CLASSIFICATION MODEL 22
53.13 59.71 56.76 50.77
ENSEMBLE APPROACH
22
53.13 55.63 55.16 52.71
Discussion
This study was based on unsupervised learning and therefore the possibility of external validation was very limited. The main aim of studies like this is to find the insight of the topics and encourage new studies which are a continuous part of this existing study. Most of the reputed case study papers were primarily based on internal validity and construct validity, but not on external validity 23.

By comparing the sentiment predictions of different methods, VADER Sentiment prediction and TextBlob predict the output almost in the same pattern (both are lexicon-based analysis) (Refer Table 4 ; Table 5). A change in prediction pattern is observed only when a number of topics is greater (Refer Table 6). However, AFINN method is a wordlist-based sentiment classification, which frequently shows different outputs compared to the other two methods (Refer Table 4, Table 5 ;Table 6)
Accuracy comparison is another area which distinguishes the better method for sentiment prediction between TextBlob and VADER methods. VADER shows much higher accuracy in predicting the sentiment on normal input (non-labeled) compared to TextBlob. VADER, which shows around 76 % accuracy compared to 24 % accuracy of TextBlob Sentient prediction (Refer Table 7 and Table 8)
However, interestingly the accuracy of the VADER sentiment analysis is decreasing when there is an increase in the number of topics in LDA modeling. In the case of TextBlob, it shows a steady accuracy in prediction of sentiment even though the accuracy rate is much lower than the VADER method.

Another interesting fact is that the accuracy rate of VADER is even higher in the labeled dataset (Table 9) compared to other renowned methods like Logistic Regression, SVM model, Naive Bayes model, Random Forest model, Decision Tree Classification model, and Ensemble Approach Using Voting Classifier 22. All these methods are well known for supervised learning. Other than VADER and TextBlob analysis, Random Forest gives a much higher accuracy rate compared to all the other methods (Refer Table 9).
VADER shows high accuracy rate in both labeled and unlabeled data sentiment analysis. However TextBlob accuracy indicates TextBlob results are undependable for this study. Other methods are used here for cross-validation and comparison purposes only and do not involve deep in this particular research.

This study result gives an insight into new policymaking on different topics which are related to city services and helps to review an overall impulse of people’s reaction on topics related to Dublin. Here, internal validation happens by comparing the results of one method of study with another. For example, results of VADER analysis are compared with results of AFINN or TextBlob and vice versa.

As a contradiction to the above validation explanations, an attempt was made to use external validation using manual tagging and supervised learning, which helps to cross-validate the results and give double authentication to this piece of research.

Conclusion & Future work
Data preprocessing and cleaning is a vital part of most of the Machine Learning projects and it is also clearly mentioned in CRISP-DM lifecycle. The data set is in metadata format and the major challenge in this study involved in data scrapping and arranging the data in further usable format. In this study, Gensim library had a vital role in processing this large amount of data and LDA topic modeling helped to get the most sophisticated topic modeling with less chance of topic irregularities in modeling. The prime advantage of this study is that it points out the topic which is important for the public and how the public reacts to each of the topics.

The objective of this study was to get the sentiment of people regarding city services. Results obtained indicate that people are mostly happy about current services and the same trend is followed even if the number of topics is increased or decreased. Only a few areas need improvement and more care. However, it is not 100 % true that all topics extracted after LDA modeling are considered for final analysis. Using inclusion and exclusion it is found that topic number 4 in 4-LDA topic modeling is ignored in the final analysis. Only a few areas like schooling, gaming and old buildings are showing some needs for improvement and people are expressing the negative sentiment in these areas.
This study indicates that there are some improvements are needed in a few areas, it helps practitioners to understand the lagging areas and help them to put some effort to resolve those. However, the practitioners need further insight into the depth and reason behind the problems and how to improve the lagging areas.
In this research, papers from different research areas like sentiment analysis and topic modeling were combined and discussed. Also, a sample project presentation paper was taken for a better understanding of the smart city project(Refer Related-Works above).It is clearly visible that the importance of sentiment analysis is increasing day by day and the scope of it is vast and wide 2. It is understood that topic modeling is an integral part of sentiment analysis and most of the researchers prefer the LDA method for it (Refer to24, 25 for detailed topic model reviews and comparison). The basic goal of this research is to help create a smart city project by applying the most appropriate topic modeling and to run a sentiment analysis in order to find a better actionable knowledge and decision support mechanism. While comparing with previous studies mentioned in related the works, the majority were emphasized on topics such as “How to do smart city analysis and future scope of smart cities in the economic and social area of life”. However, this study is more concentrated on the practical side of sentiment analysis and points out which specific areas need more focusing on to move forward to the smart city position.

However, the main limitation of this study was to filter out the data which relate only to city services. This was mainly due to the vast scope and availability of data. The second major limitation of this study was related to the accuracy of topic prediction. Even though LDA is a proven method, sometimes it also fails to showcase the related words in same topics which results in a decrease in the dependability and reliability of topics. This study helped to find the main topics where people have discomfort but did not cover the reasons behind this discomfort in depth.

The sentence by sentence sentiment analysis table helps future studies to categorize this topic further into different sessions and boost up further studies on smart city development. The scope of this study varies from person to person and as per their requirements. The main advantage of this study is that it covered all areas of machine learning except image analysis and neural networks. A study as an extension to this which can predict the reason for the negative sentiment and resolving measures has a wide scope in smart city studies. This study is a halfway mark to that ultimate aim.The next step was to screen titles and abstracts to deter-
mine which ones to accept for full article screening. This
was accomplished using two reviewers who rated each
article for inclusion or exclusion based on predefined cri-
teria. Disagreements among raters were settled by a third
reviewer. Inclusion criteria were as follows (i) Objective or
self-report measurement of physical activity or body mass
(e.g. height and weight, skin-fold or waist circumference);
(ii) Measurement, either perceived (e.g. participant self-
report) or objective (e.g. geographic information systems
GIS mapping of objective environmental data or neigh-
bourhood audits) of at least one of the 10 smart growth
principles and (iii) Publication in a peer-reviewed journal.

Exclusion criteria were (i) Papers that focused primarily on
socioeconomic characteristics of a geographic area, neigh-
bourhood problems, social cohesion, social capital, or total
city or town size; (ii) A target population consisting mostly
of senior citizens (because of functional limitations that
may limit their physical activity); (iii) Instrument validation
studies; (iv) Papers that were reviews, case reports, edito-
rials, commentaries, discussions or letters and (v) Behav-
ioural interventions without an environmental component
(e.g. walking programmes, fitness education classes, etc.).

Full articles of the accepted titles and abstracts were then
screened using the same dual rater system and against the
same criteria.

The next step was to screen titles and abstracts to deter-
mine which ones to accept for full article screening. This
was accomplished using two reviewers who rated each
article for inclusion or exclusion based on predefined cri-
teria. Disagreements among raters were settled by a third
reviewer. Inclusion criteria were as follows (i) Objective or
self-report measurement of physical activity or body mass
(e.g. height and weight, skin-fold or waist circumference);
(ii) Measurement, either perceived (e.g. participant self-
report) or objective (e.g. geographic information systems
GIS mapping of objective environmental data or neigh-
bourhood audits) of at least one of the 10 smart growth
principles and (iii) Publication in a peer-reviewed journal.

Exclusion criteria were (i) Papers that focused primarily on
socioeconomic characteristics of a geographic area, neigh-
bourhood problems, social cohesion, social capital, or total
city or town size; (ii) A target population consisting mostly
of senior citizens (because of functional limitations that
may limit their physical activity); (iii) Instrument validation
studies; (iv) Papers that were reviews, case reports, edito-
rials, commentaries, discussions or letters and (v) Behav-
ioural interventions without an environmental component
(e.g. walking programmes, fitness education classes, etc.).

Full articles of the accepted titles and abstracts were then
screened using the same dual rater system and against the
same criteria.

The next step was to screen titles and abstracts to deter-
mine which ones to accept for full article screening. This
was accomplished using two reviewers who rated each
article for inclusion or exclusion based on predefined cri-
teria. Disagreements among raters were settled by a third
reviewer. Inclusion criteria were as follows (i) Objective or
self-report measurement of physical activity or body mass
(e.g. height and weight, skin-fold or waist circumference);
(ii) Measurement, either perceived (e.g. participant self-
report) or objective (e.g. geographic information systems
GIS mapping of objective environmental data or neigh-
bourhood audits) of at least one of the 10 smart growth
principles and (iii) Publication in a peer-reviewed journal.

Exclusion criteria were (i) Papers that focused primarily on
socioeconomic characteristics of a geographic area, neigh-
bourhood problems, social cohesion, social capital, or total
city or town size; (ii) A target population consisting mostly
of senior citizens (because of functional limitations that
may limit their physical activity); (iii) Instrument validation
studies; (iv) Papers that were reviews, case reports, edito-
rials, commentaries, discussions or letters and (v) Behav-
ioural interventions without an environmental component
(e.g. walking programmes, fitness education classes, etc.).

Full articles of the accepted titles and abstracts were then
screened using the same dual rater system and against the
same criteria.

The next step was to screen titles and abstracts to deter-
mine which ones to accept for full article screening. This
was accomplished using two reviewers who rated each
article for inclusion or exclusion based on predefined cri-
teria. Disagreements among raters were settled by a third
reviewer. Inclusion criteria were as follows (i) Objective or
self-report measurement of physical activity or body mass
(e.g. height and weight, skin-fold or waist circumference);
(ii) Measurement, either perceived (e.g. participant self-
report) or objective (e.g. geographic information systems
GIS mapping of objective environmental data or neigh-
bourhood audits) of at least one of the 10 smart growth
principles and (iii) Publication in a peer-reviewed journal.

Exclusion criteria were (i) Papers that focused primarily on
socioeconomic characteristics of a geographic area, neigh-
bourhood problems, social cohesion, social capital, or total
city or town size; (ii) A target population consisting mostly
of senior citizens (because of functional limitations that
may limit their physical activity); (iii) Instrument validation
studies; (iv) Papers that were reviews, case reports, edito-
rials, commentaries, discussions or letters and (v) Behav-
ioural interventions without an environmental component
(e.g. walking programmes, fitness education classes, etc.).

Full articles of the accepted titles and abstracts were then
screened using the same dual rater system and against the
same criteria.

The next step was to screen titles and abstracts to deter-
mine which ones to accept for full article screening. This
was accomplished using two reviewers who rated each
article for inclusion or exclusion based on predefined cri-
teria. Disagreements among raters were settled by a third
reviewer. Inclusion criteria were as follows (i) Objective or
self-report measurement of physical activity or body mass
(e.g. height and weight, skin-fold or waist circumference);
(ii) Measurement, either perceived (e.g. participant self-
report) or objective (e.g. geographic information systems
GIS mapping of objective environmental data or neigh-
bourhood audits) of at least one of the 10 smart growth
principles and (iii) Publication in a peer-reviewed journal.

Exclusion criteria were (i) Papers that focused primarily on
socioeconomic characteristics of a geographic area, neigh-
bourhood problems, social cohesion, social capital, or total
city or town size; (ii) A target population consisting mostly
of senior citizens (because of functional limitations that
may limit their physical activity); (iii) Instrument validation
studies; (iv) Papers that were reviews, case reports, edito-
rials, commentaries, discussions or letters and (v) Behav-
ioural interventions without an environmental component
(e.g. walking programmes, fitness education classes, etc.).

Full articles of the accepted titles and abstracts were then
screened using the same dual rater system and against the
same criteria.

ACKNOWLEDGMENT
I would like to thank my supervisor, D.Sc. Antti Knutas for the support and direction in helping me to complete this research and document. I would also like to thank Dr. John Breslin (NUIG) for provide the access to the SIOC Corpus database.

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