Android and IOS popularity prediction using tweets .

1. TRIBHUWAN UNIVERSITY
INSTITUTE OF ENGINEERING
PASCHIMANCHAL CAMPUS
“ Online Social Network Analysis
Using Machine Learning Techniques ”
PRESENTED BY:
HARI K.C.
072/ MSCK /R/403
Department Of Electronics and Computer Engineering
A Final Project Presentation
On
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2. Introduction
 Social Networking (Twitter)
 Natural Language Processing
 Machine Learning
 Opinion Mining
 Comparison and Popularity Scoring
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3. Problem Statement
 Use of Internet ,website and social networking is in rise.
 Social media , a source of large mix type and unstructured
information.
 Difficult for the people to get the efficient , reliable and
information in less time.
 Problems while making the decision .
 This Project compare and predict based on user opinion for
effective buying decision and saves time. 3

4. Objectives
 To determine the features and opinion sentiments
using Natural Language Processing.
 To determine accuracy of Machine Learning
Classifiers.
 To compare the popularity of two Smartphone.
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5. Social Networks
 A network of social actors with interaction.
 People, Organization and Celebrities create their profiles and
share information.
 Different information about products, events, politics, stocks
and so on.
 Twitter is third most popular popular social networking sites
with 310,000,000 visitors.
 Released in July,2006 A.D.
 Real time tweets are posted up to 140 characters.
Source: WWW.ebizma.com (The 15 most popular social networking sites, Nov 26,2016)5

6. Natural Language Processing
 A field of Artificial Intelligence, focus on developing system to
allow computer- human communication using natural
language.
 It makes the computer to understand and process natural
language .
 There are various aspects of Natural Language Processing
such as  Tokenization
 Part of Speech Tagging
 Term frequency-Independent Document Frequency
 Text Classification
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7. Machine Learning
 A construction and study of system that can learn from
data.
 Deals with representation and generalization.
 Different classification and clustering algorithms.
 Supervised Machine learning technique - Classification
 Unsupervised Machine learning technique – Clustering
 Classifiers
 Naive Bayes
 Support Vector
 Logistic Regression
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8. Opinion Mining
 A study and analysis of people’s opinions, sentiments, evaluations,
appraisals towards various products, services and organization.
 It focus mainly on Positive and Negative opinions.
 3 levels of Opinion Mining
 Document level
 Sentence Level
 Aspect level
 Opinion Mining is a Natural language Processing problem.
 Opinion is Quintuple. O=(E,A,S,H,T)
 Types of Opinions
 Regular Opinions .Eg: The picture quality of Iphone7 plus
is great.
 Comparative Opinions. Eg: An Android Phone User are
more happier than IPhone User.
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9. Popularity Prediction
 An ability of Framework to forecast popularity of some
online content.
Expansion of Web and Social Networking sites allows
fast spreading of information around the globe.
Predicting Popularity is the recent research trend
E.g: Predicting US election win 2016.
Predicting Online News Popularity
Predicting weather
 Popularity Based on Features.
Metrics used for prediction is Message characteristics
such as sentiment index and relative strength.
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10. Methodology
Figure1: Block diagram of Project system
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11. Methodology
continue…..
 Feature Extraction using Twitter API
 Feature Selection using Natural Language Processing
 Classification includes Machine Learning Technique
 Polarity identification using supervised method
 Popularity score obtained using sentiment index and
Relative strength
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12. Methodology
i) Sentiment Differences= (Number of Positive Tweet – Number of Negative Tweet)
, Total number of Tweets
ii) Ratio Comparision, Rp= Number of Positive Tweets
Total Number of Tweets
Rn = Number of Negative Tweets
Total Number of Tweets
iii) Sentiment Index I(sentiment) = [Sentiment Differences + 0.5 ] * 100%
2
iv) Post Rate Rate(post)= Number of Total Tweets
Time of sample window
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13. Results
Source: https://twitter.com/@SamsungMobile
Source: https://twitter.com/@Iphone_New
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14. Results
1) Tweets as extracted from twitter using Twitter Application Programming Interface.
.
2)Tweets features for analysis
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15. Results
3)Training sets and Testing sets sample
4) Output tweets with sentiment value and confidence level
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16. Results
5) Analyzed Tweets with required features
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17. Results
6) Classifiers accuracy
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18. Results
6) Classifiers accuracy
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19. Results
7) Word Features
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20. Results
8) Most Important Features
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21. Results
8) Histogram Plots and Line Graph
a) Android SmartPhone
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22. Results
8) Histogram Plots and Line Graph
a) Android SmartPhone
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23. Results
8) Histogram Plots and Line Graph
a) IOS Iphone SmartPhone
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24. Results
8) Histogram Plots and Line Graph
a) IOS Iphone SmartPhone
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25. Results
9) Statistical values:
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26. Results
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27. Results
27Android Sentiment index=55.1% IOS Sentiment index=44.9%

28. Results
28Relative strength of Android is 40.8% which is greater than that of IOS(33.3%)

29. References
[1] A. Agarwal, B. Xie, L. O. Rumbhu and R. Passonnea, "Sentiment Analysis of Twitter Data," in Workshop on
Languages in Social Media,Computational Linguistic Association, 2011.
[2] B. Liu, Sentiment Analysis and Opinion Mining, Morgan and Claypool Publishers, May ,2012.
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Classifier for Language Learning," in DOI:10.11.1109/IISA .6623713, 2013.
[4] L. Bing, K. C. Chan and C. Ou, "Public Sentiment Analysis in Twitter Data for Prediction of Company Stock
Price Movements," in DOI: 10.1109/ICEBE, IEEE, 2014.
[5] F. Gelli, T. Urichio, M. Bertini, A. D. Bimbo and S. Fuchang, "Image Popularity Prediction in Social Media
using Sentiment and Context Features," in ACM, 2015.
[6] A. Ghuibi, S. Mohammed and S. Alshomrani, "A Comprehensive survey on web content Extraction Algorithms
and Techniques," in ICISA, 2013.
[7] N. Kaji and M. Kitsureguewa, "Building Lexicon for sentiment analysis from massive collection of html
documents," in Conference on Empirical Methods in NLP(EMNLP-CONLL), 2007.
[8] K. Lee, A. Agarwal and A. Chaudhary, "Real Time Disease Survillence using Twitter Data," in
DOI:10.1145/2487575, 19th ACM SIGKDD, ICKDDM, 2014.
[9] J. A. P and V. D. Katkar, "Sentiment analysis of twitter data using data mining," in International conference on
Information Processing, 2015.
[10] H. Schoen, D. G. Avello, P. T. Metaxas, E. M. M. Strohmaier and P. Glorr, "The Power of Prediction with Social
Media," in Internet Research,VOl.23 Iss: 5 pp 528-543 ,10.1108/IntR, 2013.
[11] S. Shahhedari, H. and M. R. Bin Daud, "Twitter Sentiment Mining: A multi Domain Analysis," in CSIS, 7th
International Conference,IEEE, 2013.
[12] K. Shimada, S. Insoune, N. Maeda and J. Endo, "Analysing Tourism on Twitter for a Local City.," in DOI:
10.1109/SSNE, 2011. 29

30. ***THANK YOU***
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31. Any Questions???
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