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A New SVM Method for Recognizing Polarity of Sentiments in Twitter
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Author(s): Sanjiban Sekhar Roy (VIT University, India), Marenglen Biba (University of New York – Tirana, Albania), Rohan Kumar (VIT University, India), Rahul Kumar (VIT University, India)and Pijush Samui (NIT Patna, India)
Copyright: 2017
Pages: 11
Source title:
Handbook of Research on Soft Computing and Nature-Inspired Algorithms
Source Author(s)/Editor(s): Shishir K. Shandilya (Bansal Institute of Research and Technology, India), Smita Shandilya (Sagar Institute of Research Technology and Science, India), Kusum Deep (Indian Institute of Technology Roorkee, India)and Atulya K. Nagar (Liverpool Hope University, UK)
DOI: 10.4018/978-1-5225-2128-0.ch009
PurchaseView on the publisher's website for pricing and purchasing information.
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Abstract
Online social networking platforms, such as Weblogs, micro blogs, and social networks are intensively being utilized daily to express individual's thinking. This permits scientists to collect huge amounts of data and extract significant knowledge regarding the sentiments of a large number of people at a scale that was essentially impractical a couple of years back. Therefore, these days, sentiment analysis has the potential to learn sentiments towards persons, object and occasions. Twitter has increasingly become a significant social networking platform where people post messages of up to 140 characters known as ‘Tweets'. Tweets have become the preferred medium for the marketing sector as users can instantly indicate customer success or indicate public relations disaster far more quickly than a web page or traditional media does. In this paper, we have analyzed twitter data and have predicted positive and negative tweets with high accuracy rate using support vector machine (SVM).
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