Sentiment Classification for Beauty-fashion Reviews

Abstract

The fast growth of e-commerce markets helps companies bring their products closer to customers and lets users have many choices for online shopping. However, it causes the need to have a proper strategy to keep customers in every company. As a rising solution, sentiment analysis on users’ feedback using artificial intelligence is a timely-fashioned way for business owners to understand their customers and clients, which could help them improve their business against competitors. Therefore, in the scope of our research, we introduce our results on the task of customers’ review sentiment analysis using the dataset provided in the Fashion and Beauty Review Rating (one competition organized in Kaggle), where our solution reached first place with a score of 0.51269 RMSE. Our proposed solution combines deep learning models (Bidirectional Long Short-term Memory, Bidirectional Gated Recurrent Unit, Convolutional Neural Network) and a rule-based method (a method that uses linguistic rules to predict the rating of reviews). We can describe the solution in this paper with the support of analysis techniques to give more insightful points.