open access

EFFICIENT HIGH COVERAGE SOCIAL MEDIA OPINION ANALYSIS USING HASH TAGGER APPROACH

  • Jeevananthem R Department of Computer Science and Engineering, Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Sriram A Department of Computer Science and Engineering, Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Sudhakar R Department of Computer Science and Engineering, Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Vijay C Department of Computer Science and Engineering, Nandha College of Technology, Erode-638052, Tamilnadu, India
  • Sudhakar R Assistant Professor, Department of Computer Science and Engineering, Nandha College of Technology, Erode-638052, Tamilnadu, India

Abstract

This work proposes a novel Sentiment-Based Enhanced Naïve Bayes to address the information overload problem through information filtering. The proposed framework first applies a Natural Language Processing (NLP) technique to perform sentiment analysis taking advantage of the huge sums of textual data generated in from the social media are predominantly left untouched. Although some current studies do employ review texts, many of them do not consider how sentiments in reviews influence recommendation algorithm for prediction.


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