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Sentiment Analysis and Review Rating Prediction of the Users of Bangladeshi Shopping Apps
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Author(s): Md Shamim Hossain (Hajee Mohammad Danesh Science and Technology University, Bangladesh)and Mst Farjana Rahman (Hajee Mohammad Danesh Science and Technology University, Bangladesh)
Copyright: 2022
Pages: 24
Source title:
Developing Relationships, Personalization, and Data Herald in Marketing 5.0
Source Author(s)/Editor(s): Jasmine Kaur (Chitkara Business School, Chitkara University, Punjab, India), Priya Jindal (Chitkara Business School, Chitkara University, India)and Amandeep Singh (Chitkara Business School, Chitkara University, India)
DOI: 10.4018/978-1-6684-4496-2.ch002
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Abstract
The goal of this study is to apply machine learning (ML) approaches to assess user sentiment and predict review ratings for Bangladeshi shopping apps. The data for this study was obtained from the Google Play Store reviews of 15 Bangladeshi shopping apps. The AFINN and VADER sentiment algorithms were used to assess the filtered summary phrases as positive, neutral, or negative sentiments after cleaning. The present study additionally employed five supervised machine learning approaches to divide users' assessments of shopping apps into three sentiment groups. According to the findings of this survey, the majority of ratings for shopping apps were positive. While all five machine learning approaches (SVC, k-neighbors classifier, logistic regression, decision tree classifier, and random forest classifier) can properly categorize review text into sentiment classes, the random forest classifier outperforms in terms of high accuracy. This study adds to the literature on customer sentiment and aids app marketers in understanding how consumers feel about apps.
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