Sentiment analysis of online product reviews based on SenBERT-CNN

Fangyu Wu, Zhenjie Shi, Zhaowei Dong, Chaoyi Pang, Bailing Zhang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings of 2020 International Conference on Machine Learning and Cybernetics, ICMLC 2020
PublisherIEEE Computer Society
Pages229-234
Number of pages6
ISBN (Electronic)9780738124261
DOIs
Publication statusPublished - 2 Dec 2020
Event19th International Conference on Machine Learning and Cybernetics, ICMLC 2020 - Virtual, Online
Duration: 4 Dec 2020 → …

Publication series

NameProceedings - International Conference on Machine Learning and Cybernetics
Volume2020-December
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference19th International Conference on Machine Learning and Cybernetics, ICMLC 2020
CityVirtual, Online
Period4/12/20 → …

Keywords

  • BERT
  • Online product review
  • Sentiment analysis
  • Word embedding

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