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Research Paper | Computer Science | Volume 2 Issue 10, October 2013 | Pages: 196 - 202 | India
Sentiment Analysis of Micro blogs using Opinion Mining Classification Algorithm
Abstract: Opinion mining and sentiment analysis is a fast growing topic with various world applications, from polls to advertisement placement. Traditionally individuals gather feedback from their friends or relatives before purchasing an item, but today the trend is to identify the opinions of a variety of individuals around the globe using micro blogging data. This paper discusses an approach where a publicized stream of tweets from the Twitter micro blogging site are preprocessed and classified based on their subjectivity word and semantic phrase content as positive, negative and irrelevant. Analyses the performance of various classifying algorithms based on their precision and recall in such cases. In this paper, focus on using Twitter, the most popular micro-blogging platform, for the task of sentiment analysis. Show how to automatically collect a corpus for sentiment analysis and opinion mining purposes. The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations and a negative semantic orientation when it has bad associations this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word excellent minus the mutual information between the given phrase and the word poor. Experimental evaluations show that proposed techniques are efficient and perform better than previously proposed methods.
Keywords: opinion mining, part of speech, unigram, bigram
How to Cite?: A. Tamilselvi, M. ParveenTaj, "Sentiment Analysis of Micro blogs using Opinion Mining Classification Algorithm", Volume 2 Issue 10, October 2013, International Journal of Science and Research (IJSR), Pages: 196-202, https://www.ijsr.net/getabstract.php?paperid=02013335, DOI: https://dx.doi.org/10.21275/02013335