Sentiment Analysis for Online Reviews for Brand Imaging and Customer Retention
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© 2023 by IJCTT Journal
Volume-71 Issue-12
Year of Publication : 2023
Authors : Karan Gupta, Amit Bhanushali
DOI :  10.14445/22312803/IJCTT-V71I12P102

How to Cite?

Karan Gupta, Amit Bhanushali, "Sentiment Analysis for Online Reviews for Brand Imaging and Customer Retention," International Journal of Computer Trends and Technology, vol. 71, no. 12, pp. 5-11, 2023. Crossref, https://doi.org/10.14445/22312803/IJCTT-V71I12P102

Abstract
Reviews play an essential role in understanding information about events, places, or any commercial product. These reviews are captured simply on a numeric scale, or they can be elaborated in detailed text reviews. In this study, we analyze if we can summarize the tremendous textual criticism by trying to predict ratings on a 5-point scale using reviews provided by users. We also perform sentiment analysis to understand if the sentiments in the review are following the user star rating provided. Our findings show that star ratings can be predicted to some extent based on the review comments. The analysis can be leveraged to check the performance of restaurants on different platforms and track the performance of the restaurants over the years, which will be beneficial to summarize the review comment to the star rating and save people time to read through the entire review description. Also, it could be used to extrapolate and retain customers from the business perspective of restaurants.

Keywords
Sentiment analysis, ETL, Classification, Big data.

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