1. SOCIAL COMPUTING Homework 3 Presentation
By Arun Sharma

2. Data Chosen The Data Chosen is Twitter Data from Profiles of People.
Includes their Tweet Text, Number of Followers, Retweets etc.
Used for Collection process: Tweepy Twitter Library (Lets you search for specific twitter handles/ hashtags based on your query)

3. Data Collected from Established Leaders: Barack Obama Narendra Modi
Praveen Swami
Amitabh Bachchan
Sachin Tendulkar

4. Data Collected From
Self Made Leaders: Used an article from India today to collect famous twitter celeb names
the-twitter-god/280458
Gabbar Singh
Ikaveri
Jaihind
Jhunjhunwala
GreatBong
RoflIndian

5. ESTABLISHING GROUND TRUTH
Used 3 Human Annotators for each specific group of people.
Annotators were asked to fill out a form based on a subset of data provided to them.
The majority decision on the annotated tweets on various parameters was taken as ground truth.
In case of a tie, another human annotator was consulted. And the majority decision was considered.

6. ESTABLISHING GROUND TRUTH
For Every Tweet The Annotators had to Choose from the below mentioned options
Positive
Negative
Neutral

7. Features Chosen And How to Extract Them
I worked on the following Features:
Sentiment Involved: Positive/Negative/Neutral
Unigram/Bigram/Trigram/Line Length
Pronoun Count (Stanford Core NLP & Code from HW1)
Used an existing open source tool like Weka/LightSide to predict sentiment based on given annotated data collected during Ground Truth.

8. Experimental Methodology
Targeted Data Collection using Tweepy was done.
Data Cleaning
Establishing Ground Truth using Human Annotators on a subset of chosen data
Extracting the Relevant and chosen features using tools available
Analyzing the results and comparing them for different groups chosen.

9. Ground Truth
Using human annotators the following count of sentiment was obtained
Total Tweets: 526
Positive Tweets: 304
Negative Tweers: 165
Neutral Tweets: 87

10. Classifier For Sentiment Extraction
I used Support Vector Machine to train and predict my data.
In the annotated data that was used to train the classifier about the percentage of 60% (approximately) were positive the rest being negative and neutral.
A 15 fold cross validation was performed on the dataset.

11. Precision and Recall Obtained
The precision and recall values as obtained for the Positive, Negative and Neutral values is as follows
The following results show high precision and recall for positive prediction and less for the other two.
This is due to the case that the data collected had very few negative and neutral examples to train on when compared to the positive example tweets.
Sentiment
Precision
Recall
Positive
74.917
72.99
Negative
47.979
49.686
Neutral
45.977
47.059

12. Dataset 1: Sentiment Sentiment Extracted after applying Classifier
Total Positive Identified
450
Total Negative Identified
154
Total Neutral Identified
114
Total Tweets
717

13. Dataset 1: Sentiment Sentiment Extracted after applying Classifier
Total Positive Identified
361
Total Negative Identified
306
Total Neutral Identified
234
Total Tweets
900

14. Pronoun Count: Dataset 1 & 2 Dataset 1 Pronoun Count Dataset 2 Obama
142
GreatBong
151
PraveenSwami
147
ikaveri
123
Narendra Modi
184
Jaihind
216
SrBachchan 92
jhunjhunwala
114
Sachin
191
roflindian
121

15. Pronoun Count: Total and Average
Dataset 1 (Established Leaders)
816
Dataset 2 (Self Made Leaders)
756
Average Pronoun count in dataset 1
163.2
Average Pronoun count in dataset 2
126

16. Likes & Retweets Total Average Dataset 1 Dataset 2 Total Likes 2843745
Dataset 1
Dataset 2
Total Likes
9830
Total Retweets
28837
Dataset 1
Dataset 2
Average Likes
568,749
1683
Average Retweets
221,180
4806

17. Likes & Retweets: Reasons
Gap is huge with respect to the number of likes
And number of retweets between the twitter users in dataset one and dataset two
Can be attributed to the fact that already established leaders on twitter cater to a larger segment of twitter population
Whereas the self established ones cater to the niche followers that they have created in their domain.

18. Number Of Followers And Following:
Total:
Average:
Dataset 1
Dataset 2
Total No. of Followers
585744
Total Number of Following
639119
4339
Dataset 1
Dataset 2
Average no. of followers/user
97624
Average no. of following/user
127823
723

19. Does Not Convey the Whole Picture: Followers and Following
Dataset 1
Dataset 2
Name of user
Followers
Following
Obama
636613
Narendra Modi
1372
Amitabh Bachchan
999
Sachin 15
Praveen Swami
56560
120
Name of User
Followers
Following
Jaihind
2210
143
Gabbar Singh
277135
1230
GreatBong
23151
140
JhunJhunwala
95667
705
RoflIndian
125883
469
ikaveri
41095
1652

20. Number Of Followers And Following: Reason
The Average no. of followers might not be as an accurate indicator
There were outlier profiles that increased this values dramatically when the others did not show such a huge numbers as they had a lot of followers of their own.

21. USER INTERACTION:
Calculated the total number of interactions made by each member of the dataset.

22. USER INTERACTION: Dataset 1 Total number of references: 397
Average Replies: 79.4 tweets per user
Dataset 2
Total number of references: 781
Average Replies: tweets per user

23. USER INTERACTION: Reasons
Established leaders interact less with public and tweet the things they think are important.
Self made leaders interact way more with their followers.
They need to interact with people in order to remain an influencer on twitter world.

24. Conclusion: The Difference Exists
As we have seen from the profiles analyzed there is a significant diffirence between both the groups.
Difference can be noticed by looking:
General Sentiments of the tweets
Use of Pronoun
Number of Followers
Number of Following
User Interactions done