The week's pick
Reseach Article
Trend Analysis on Twitter for Predicting Public Opinion on Ongoing Events
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 26 |
| Year of Publication: 2018 |
| Authors: Tejal Rathod, Mehul Barot |
10.5120/ijca2018916596
|
Tejal Rathod, Mehul Barot . Trend Analysis on Twitter for Predicting Public Opinion on Ongoing Events. International Journal of Computer Applications. 180, 26 ( Mar 2018), 13-17. DOI=10.5120/ijca2018916596
Abstract
Twitter is most popular social media that allows its user to spread and share information. It Monitors their user postings and detect most discussed topic of the movement. They publish these topics on the list called “Trending Topics”. It show what is happening in the world and what people's opinions are about it. For that it uses top 10 trending topic list. Some topic will trend at some point in the future and others will not. We wish to predict which topics will trend. And apply algorithm to find out what public opinion about that topic which use to predict mood. In this paper, we propose model which use machine learning algorithm and classify sentiment of twitter message. For that we collect tweet, preprocess that tweet, find trending topic and apply multi classifier algorithm which predict public mood. We are going to use different measure such as precision, recall, F-measure. We will going to achieve better accuracy.
References
- Luca Maria Aiello, Georgios Petkos, Carlos Martin, David Corney, Symeon Papadopoulos, Ryan Skraba, Ayse Göker, Ioannis Kompatsiaris, Senior Member “Sensing Trending Topics in Twitter” IEEE, and Alejandro Jaimes IEEE Transactions On Multimedia, Vol. 15, No. 6, October 2013.
- Soyeon Caren Han, Hyunsuk Chung, Do Hyeong Kim, Sungyoung Lee, and Byeong Ho Kang “Twitter Trending Topics Meaning Disambiguation” Springer International Publishing Switzerland 2014.
- Arkaitz Zubiaga, Damiano Spina, Raquel Mart´ınez, V´ıctor Fresno “Real-Time Classification of Twitter Trends” Journal of the American Society for Information Science and Technology copyright @ 2013.
- Altawaier, M. M., & Tiun, S. (2016) “Comparison of Machine Learning Approaches on Arabic Twitter Sentiment Analysis” International Journal on Advanced Science, Engineering and Information Technology, 6(6), 1067-1073.
- Rong Lu and Qing Yang, “Trend Analysis of News Topics on Twitter”,International Journal of Machine Learning and Computing Vol. 2, No. 3, June 2012
- Kathy Lee, Diana Palsetia, Ramanathan Narayanan, Md. Mostofa Ali Patwary, Ankit Agrawal, Alok Choudhary, “Twitter Trending Topic Classification” 2011 11th IEEE International Conference on Data Mining.
- Erwin B. Setiawan, Dwi H. Widyantoro, Kridanto Surendro, “Feature Expansion using Word Embedding for Tweet Topic Classification” IEEE, 2016.
- http://www.socialmediatoday.com/social-networks/heres-why-twitter-so-important-everyone
- http://www.newsmedialive.com/wpcontent/uploads/2015/10/TWITTER.jpg
- http://www.twitter.com
- Yubao Zhang, Student Member, IEEE, Xin Ruan, Student Member, IEEE, Haining Wang, Senior Member, IEEE, Hui Wang, and Su He “Twitter Trends Manipulation: A First Look Inside the Security of Twitter Trending” IEEE transactions on information forensics and security, vol. 12, no. 1, january 2017.
- Amina Madani, Omar Boussaid, Djamel Eddine Zegour “Real-time trending topics detection and description from Twitter content” Springer-2015.
- Arkaitz Zubiaga, Damiano Spina, Raquel Martinez, Victor Fresno, “Real-Time Classification of Twitter Trends” American Society for Information Science and Technology 2013.
- Arkaitz Zubiaga, Damiano Spina, Víctor Fresno, Raquel Martínez “Classifying Trending Topics: A Typology of Conversation Triggers on Twitter” ACM 2011.
- María del Pilar Salas-Zárate, José Medina-Moreira, Paul Javier Álvarez-Sagubay “Sentiment Analysis and Trend Detection in Twitter” Springer 2011.
- https://statinfer.com/204-6-8-svm-advantages-disadvantages-applications/?c=361cde8465e4
- https://www.slideshare.net/ashrafmath/naive-bayes-15644818
- http://www2.cs.man.ac.uk/~raym8/comp37212/main/node264.html
- A. Hernandez-Suarez, G. Sanchez-Perez, V. Martinez-Hernandez, H. Perez-Meana,K. Toscano-Medina, M. Nakano and V. Sanchez “Predicting Political Mood Tendencies based on Twitter Data”
- http://www.kdnuggets.com/2017/06/which-machine-learning-algorithm.html
- Amina Madani,Omar Boussaid, Djamel Eddine Zegour “Real-time trending topics detection and description from Twitter Content” Springer 2015.
- https://web.stanford.edu/class/cs276/handouts/lecture14-SVMs.ppt
- https://www.dtreg.com/solution/view/29
- https://github.com/ctufts/Cheat_Sheets/wiki/Classification-Model-Pros-and-Cons
- https://www.quora.com/What-are-applications-of-linear-and-logistic-regression
- Zgheib, W. A., & Barbar, A. M. A Study using Support Vector Machines to Classify the Sentiments of Tweets.
- Go, A., Bhayani, R., & Huang, L. (2009). Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford, 1(2009), 12.
- Munir Ahmad, Shabib Aftab, Iftikhar Ali “Sentiment Analysis of Tweets using SVM” International Journal of Computer Applications November 2017.
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