Sentiment Analysis of Product Review

Authors

  • Chandu Vaidya  Rajiv Gandhi College of Engineering and Research, Wanadongri, Nagpur, Maharashtra, India
  • Rutika Janbandhu  Rajiv Gandhi College of Engineering and Research, Wanadongri, Nagpur, Maharashtra, India
  • Sampada Dahikar  Rajiv Gandhi College of Engineering and Research, Wanadongri, Nagpur, Maharashtra, India
  • Shital Lichade  Rajiv Gandhi College of Engineering and Research, Wanadongri, Nagpur, Maharashtra, India
  • Ekta Khode  Rajiv Gandhi College of Engineering and Research, Wanadongri, Nagpur, Maharashtra, India

Keywords:

Sentiment Analysis, Naive Bayes, Mining, Support Vector Machine, Supervised approach, Unsupervised approach, Polarity, Semantic

Abstract

Sentiment analysis is defined as the process of mining of data, view, review or sentence to predict the emotion of the sentence through natural language processing (NLP). The sentiment analysis involves classification of text into three phase "Positive", "Negative" or "Neutral". It analyses the data and labels the 'better' and 'worse' sentiment as positive and negative respectively. Using social media, e-commerce website, movies reviews such as Facebook, twitter, Amazon, Flipkart etc. user share their views, feelings in a convenient way. To analysis of such huge data automatically, the field of sentiment analysis has turn up. The main aim of sentiment analysis is to identifying polarity of the data in the Web and classifying them. Therefore, to find polarity or sentiment of, user or customer there is a demand for automated data analysis techniques. In this paper, a detailed survey of different techniques or approach is used in sentiment analysis and a new technique which is proposed in this paper.

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Published

2023-06-30

Issue

Section

Research Articles

How to Cite

[1]
Chandu Vaidya, Rutika Janbandhu, Sampada Dahikar, Shital Lichade, Ekta Khode, " Sentiment Analysis of Product Review " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 3, pp.10-15, May-June-2023.