Sentiment analysis on E-commerce reviews and ratings using ML & NLP models to understand consumer behavior
Priyanshi Kathuria, Parth Sethi, Rithwick Negi · 2022
This research paper covers the sentiment analysis of fashion e-commerce products by comparing their reviews (electronic word-of-mouth), and ratings using the ML model and NLP concepts. The scope of the paper dives into the aspect of understanding consumer behavior in a virtual environment. It explores the impact of eWOM and ratings of a product, on customer attitude, and the likelihood of that product being purchased. We have further established a relationship between ratings, reviews, and product recommendations. The paper contains an exploratory analysis backed with proper reasoning. We have made use of ML classification models like logistic regression, ADA boost, SVM, naïve Bayes, and random forest on customer reviews. We have further used Vader and text blob technologies to perform sentiment analysis.