Sentiment Analysis of Online Product Reviews Based On SenBERT-CNN

Abstract

Sentiment analysis, also known as opinion mining, is an important area of research to analyze people’s opinions. In online e-commerce marketplace like Taobao, customers are allowed to comment on different products, brands and services using text and numerical ratings. Such reviews towards a product are valuable for the improvement of the product quality as they influence consumers’ purchase decisions. In this paper, we introduce a novel model, SenBERT-CNN, to analyze customer’s review. In order to capture more sentiment information in sentences, SenBERT-CNN model combines a pre-trained Bidirectional Encoder Representations from Transformers (BERT) network with Convolutional Neural Network (CNN). Specifically, we use BERT structure to better express sentence semantics as a text vector, and then further extract the deep features of the sentence through a Convolutional Neural Network. The effectiveness of the proposed method is validated through a collected product reviews of mobile phone from the e-commerce website, JD.com.