Sentiment Analysis of Product Review
Chandu Vaidya,
Rutika Janbandhu,
Sampada Dahikar,
Shital Lichade and
Ekta Khode
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 3, 10-15
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.
Keywords: Sentiment Analysis; Naive Bayes; Mining; Support Vector Machine; Supervised approach; Unsupervised approach; Polarity; Semantic (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT2390294
References: Add references at CitEc
Citations:
Downloads: (external link)
https://ijsrcseit.com/CSEIT2390294 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT2390294.pdf Full text (application/pdf)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i3:id:hcseit2390294
Access Statistics for this article
More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().