Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

Opinion Mining of Real Time Twitter Tweets

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 19
Year of Publication: 2014
Akash Shrivatava
Shweta Mayor
Bhasker Pant

Akash Shrivatava, Shweta Mayor and Bhasker Pant. Article: Opinion Mining of Real Time Twitter Tweets. International Journal of Computer Applications 100(19):1-4, August 2014. Full text available. BibTeX

	author = {Akash Shrivatava and Shweta Mayor and Bhasker Pant},
	title = {Article: Opinion Mining of Real Time Twitter Tweets},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {100},
	number = {19},
	pages = {1-4},
	month = {August},
	note = {Full text available}


Twitter is a real-time information network and micro-blogging service that allows users to post updates. The service rapidly gained worldwide popularity that connects to the latest stories, ideas, opinions, and news. It is a powerful tool for real-time way of communicating with people by combining messages that are quick to write, easy to read, public and accessible anywhere. On Twitter anyone can read, write and share messages or tweets. Opinion mining is a type of natural language processing for tracking the mood of the public about a particular product. Opinion mining, which is also called sentiment analysis, involves building a system to collect and examine opinions about the product made in blog posts, comments, reviews or tweets. Social media plays an important role in inferring the opinion of the authors. In this paper we focused on tweets that will result in analyzing the view of the public on generally discussed topics. A tweets puller is developed that automatically collects random opinions and classifier tool that performs classifications on that corpus collected from Twitter. Our classification is based on features extracted and classified into POSITIVE, NEGATIVE and NEUTRAL. The results further evaluated and concluded to infer the performance of the classification through SVM.


  • L. Cai and T. Hofmann. Text categorization by boosting automatically extracted concepts. In SIGIR '03, pages 182. 189, New York, NY, USA, 2003. ACM Press.
  • Twitter as a Corpus for Sentiment Analysis and Opinion Mining Alexander Pak, Patrick Paroubek.
  • Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews Kushal Dave, Steve Lawrence and David M. Pennock.
  • D. Zhuang, B. Zhang, Q. Yang, J. Yan, Z. Chen, and Y. Chen. Ef_cient text classi_cation by weighted proximal svm. In ICDM, pages 538. 545, 2005.
  • Ivanciuc, O. Applications of Support Vector Machines in Chemistry. (2007), Rev. Comput. Chem. , 23, 291-400.
  • Chang, C. -C. , & Lin, C. -J. , (2003), LIBSVM: a library for support vector machines.
  • Wei, Hsu, C. , Chung Chang, C. , & Chih-Jen Lin, A. , (2003), Practical Guide to Support Vector Classification.
  • Vladimir N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer-Verlag.
  • Haggai Toledano; Elad Yom-Tov; Dan Pelleg; Edwin Pednault; Ramesh Natarajan, (2008), Support Vector Machine Solvers: Large-scale, Accurate, and Fast.
  • Stephen dann, (2010), twitter content classification.
  • http://www. csie. ntu. edu. tw/~cjlin/libsvm/
  • Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Found. Trends Inf. Retr. , 2(1-2):1–135.
  • Changhua Yang, Kevin Hsin-Yih Lin, and Hsin-Hsi Chen. 2007. Emotion classification using web blog corpora. In WI '07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pages 275–278, Washington, DC, USA. IEEE Computer Society
  • Jonathon Read. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In ACL, the Association for Computer Linguistics.