Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

Li Yang,Ying Li,Jin Wang,R Simon Sherratt

doi:10.1109/access.2020.2969854

Abstract

In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

Highlights

With the rapid development and popularization of e-commerce technology, more and more users like to shop on various e-commerce platforms
In order to improve the performance of existing sentiment analysis models in the sentiment analysis field of product reviews, this paper proposes a SLCABG model based on the advantages of the sentiment lexicon and deep learning techniques
The main contributions of this paper are as follows: 1. We propose a new sentiment analysis model based on the advantages of sentiment lexicon, word vectors, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU) and the attention mechanism, and experiment on the book review dataset from the real e-commerce book website to verify the effectiveness of the model

Summary

Introduction

With the rapid development and popularization of e-commerce technology, more and more users like to shop on various e-commerce platforms. While online shopping brings convenience to consumers, due to the virtuality of the e-commerce. Platforms, there are many problems in the products sold on the platforms, such as inconsistency between descriptive information and real goods, poor quality of goods, imperfect after-sales of goods and so on [2]. It is of great significance to conduct sentiment analysis on the commodity evaluation of the purchased products on electronic commerce platforms. Analyzing the sentiment tendency of consumer evaluation can provide a reference for other consumers and help businesses on e-commerce platforms to improve service quality and consumer satisfaction

Methods

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Discussion

Conclusion