Hybrid Approach for User Reviews' Text Analysis and Visualization: A Case Study of Amazon User Reviews

Abstract

Nowadays, many people prefer to purchase through online websites. Usually, those people start with reading user reviews and comments before making a purchase decision. The user reviews are considered powerful sources of information about products, in which users share opinions and previous experiences on using these products. However, these reviews are mostly textual and uncategorized. Thus, new customers need to read a massive amount of reviews, one by one, to make a decision. This study attempts to bridge this gap and proposes a hybrid approach of topic modeling that combines supervised and unsupervised learning. In particular, the study collected a massive amount of Amazon user reviews, analyzed the reviews' texts, and combined two approaches of topic modeling, which are unsupervised and supervised learning, i.e., semi-supervised learning. Besides, the study makes classification on reviews based on sentiment analysis. The resulting reviews' topics and their sentiment classifications are displayed on a visual dashboard. The proposed hybrid approach showed better performance in terms of text analysis and clearer representation of review topics. The outcome of this study helps customers make their decision on purchase products in a more effortless and clearer way.

CNN-Bi-LSTM Model for MOOC Forum Post Classification

“…The unbalanced data distribution will seriously affect the classification results of the classification algorithm, which is also one of the main difficulties faced by text classification in the MOOC forum. Therefore, there are still many challenges in the research of text classification in MOOC forums that need to be further explored [4].…”