Feature Selection and Classification for Sentiment Analysisof Amazon Product Reviews

Authors

  • Daniel SS Dept of ComputerScience ,Dr. C.V. Raman University Kota ,C.G ,India
  • Sahu N Dept of ComputerScience ,Dr. C.V. Raman University Kota ,C.G ,India
  • Thomas A Dept of Information Technology , BITDurg , CSVT University , Bhilai, C.G, India

Keywords:

Feature selection, Sentiment classification, Categorization

Abstract

Online reviews provide accessible and plentiful data for relatively easy analysis for a given product.This paper seeks to apply and extend the current work in the field of Natural Language processing and sentiment analysis to retrieve information from Amazon Product reviews classify them using Naïve bayes classifier . This work presents a methodology that shows how text data can provide insight into various features of a product found in the customer reviews and feature selection method

References

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[3] I. Hemalatha , G. P Saradhi V., &A. Govardhan, “Preprocessing the Informal Text for efficient Sentiment Analysis” , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS),Volume 1, Issue 2,2012.

[4] Wei Gao , Fabrizio Sebastiani ,” TweetSentiment: From Classification to Quantification’,Springer-Verlag Wien 2016

[5] M. Bouazizi and T. Ohtsuki, ‘‘Sentiment analysis in Twitter: Fromclassification to quantification of sentiments within tweets,’’ inProc. IEEEGLOBECOM, Dec. 2016, pp. 1–6.

[6] Aashutosh Bhatt, Ankit Patel, Harsh Chheda, Kiran Gawande,” Amazon Review Classification and SentimentAnalysis”, International Journal of Computer Science and Information Technologies, Vol. 6 (6) , 2015, 5107-5110

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Published

2025-11-24

How to Cite

[1]
S. S. Daniel, N. Sahu, and A. Thomas, “Feature Selection and Classification for Sentiment Analysisof Amazon Product Reviews”, Int. J. Comp. Sci. Eng., vol. 7, no. 3, pp. 173–177, Nov. 2025.

Issue

Vol. 7 No. 3 (2019): IJCSE Special Issue Feb Edition

Section

Review Article

License

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