Better Decision Making with Sentiment Analysis of Amazon reviews

• Published in: 2021 International Conference on Information Systems and Advanced Technologies (ICISAT) pp. 1 - 7
• Main Authors: Wedjdane, Nahili, Khaled, Rezeg, Okba, Kazar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27-12-2021
• Subjects: amazon reviews; comparative experiment; Decision making; deep learning; Electronic commerce; Memory; Sentiment analysis; Social networking (online); Sociology; Statistical analysis; text mining
• DOI: 10.1109/ICISAT54145.2021.9678483

The way we purchase products online has been revolutionized due to disruptive digital technologies making all the information available at our fingertips. This paper inspects the effect of reviews on mobile phones sales. Noting the inconsistent results on the effect of reviews in previous literature, this study examines how the sentiments on different mobile phone brands in online reviews affect their sales. In our work, we analyze reviews and comments about smartphones from Amazon as data set and classify the reviews text by negative/positive, very negative/very positive, and neutral sentiment. The information contained in reviews is very helpful for both the shoppers and product makers. In this paper, we conclude the following unique characteristics through more than 400,000 real smartphone reviews: (1) Short average length; (2) Power-law distribution; (3) Large span of length; (4) Notable difference in sentiment polarity. Based on the characteristics mentioned above, a series of comparative experiments have been done for sentiment classification and our model achieved an accuracy score of 76.80%