Multi-Class Tweet Categorization Using Map Reduce Paradigm
Mohit Tare , Indrajit Gohokar , Jayant Sable , Devendra Paratwar , Rakhi Wajgi."Multi-Class Tweet Categorization Using Map Reduce Paradigm". International Journal of Computer Trends and Technology (IJCTT) V9(2):78-81, March 2014. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
Twitter is one of the most popular micro-blogging website in today`s globalized world. Twitter messages can be mined to gain valuable information. Although Twitter provides a list of most popular topics people tweet about known as Trending Topics in real time, it is often hard to understand what these trending topics are about. Therefore, various efforts are being made to classify these topics into general categories with high accuracy for better information retrieval. We propose the use of one of the classification algorithm called Naïve Bayes for the categorization of tweets which has been discussed in this paper. It then proposes how the Map – Reduce paradigm can be applied to existing Naïve Bayes algorithm to handle large number of tweets.
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Categorization, Map-Reduce, Trending Topics, Tweet, Twitter