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Opinion Mining of Real Time Twitter Tweets

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 19
Year of Publication: 2014
Authors:
Akash Shrivatava
Shweta Mayor
Bhasker Pant
10.5120/17630-0691

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

@article{key:article,
	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}
}

Abstract

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.

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