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A Novel Reason Mining Algorithm to Analyze Public Sentiment Variations on Twitter and Facebook

Ashwini Patil, R.R. Sedamkar, Shiwani Gupta. Published in Algorithms

International Journal of Applied Information Systems
Year of Publication: 2015
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Ashwini Patil, R.R. Sedamkar, Shiwani Gupta
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  1. Ashwini Patil, R R Sedamkar and Shiwani Gupta. Article: A Novel Reason Mining Algorithm to Analyze Public Sentiment Variations on Twitter and Facebook. International Journal of Applied Information Systems 10(3):23-29, December 2015. BibTeX

    	author = "Ashwini Patil and R.R. Sedamkar and Shiwani Gupta",
    	title = "Article: A Novel Reason Mining Algorithm to Analyze Public Sentiment Variations on Twitter and Facebook",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 10,
    	number = 3,
    	pages = "23-29",
    	month = "December",
    	note = "Published by Foundation of Computer Science (FCS), NY, USA"


With the explosive growth of social media on web, analyzing Public Sentiment Variations (PSV) has become utmost necessity as public sentiments fluctuate with alterations in real life events. Analysis of PSV empowers decision makers to gain a better understanding of public reactions in social, political and economic environment thus helps in better decision making. Most of the recent studies are bounded to be analyzing and predicting public sentiments. In this paper, we have done further analysis to know the useful insights for PSV using tweets and Facebook comments with a specific target. We propose a Novel Reason Mining Algorithm to find the possible reasons affecting PSV in significant sentiment variation period. It uses incremental iterative approach to refine the final list of most influential reasons. To give more intuitive representation, the algorithm ranks a set of mined reason candidates. The reason candidate with the most number of tweets/comments is the main reason for sentiment variation in that time period. The quality of mined reasons mainly depends on the quality of clusters formed prior to reason mining. The performance of our approach is compared with baseline K-means clustering technique. Experimental results show that our approach forms better clusters and effectively mine the reasons for sentiment variations. The K-means technique attained 57.05% precision and 53.31% recall on Twitter and 69.86% precision and 40.62% recall on Facebook. The Novel Reason Mining Algorithm achieved 79.4 % precision and 44.45% recall on Twitter and 71.13 % precision and 40.62% recall on Facebook.


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Public Sentiment Variation, Novel Reason Mining Algorithm, Twitter, Facebook