A Novel approach to Time based Collective User Sentiment Analysis for Twitter Data â€“ A BigData perspective
Lija Mohan, Sudheep Elayidom
International Journal of Web Applications, Vol. 9, No. 3, 2017.
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Abstract:
Today, data is evolving at a massive rate and social media contributes to it by adding, peopleâ€™s views, ideas,
interests, day today happenings etc. Hence, analyzing social media contents will excavate knowledge treasures in the form
of customer behavior, feedback, suggestions, opinions etc which could be utilized for business intelligence. Even though,
sentiment analysis from social media is a severely explored domain, this article studies the importance of â€œTime of posting the
contentsâ€. There are several real world applications where mere sentiment analysis is not sufficient and â€˜timeâ€™ at which that
content is posted should be given a weightage. For eg., to compare two products or to do trend analysis, recent posts should
be given more weightage. The authors introduce a novel and scalable algorithm to introduce the time factor to improve the
accuracy of sentiment analysis. To support Big Data, Hadoop Map Reduce based implementation is provided. To prove the
efficiency of the method, Delhi Assembly Election Winner Prediction by Twitter Analysis is taken as a case study. The results
prove that, our algorithm is accurate, scalable and time efficient as compared to the existing ones.