Better Decision Making with Sentiment Analysis of Amazon reviews

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%