Performance Evaluation of Text Embeddings with Online Consumer Reviews in Retail Sectors

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

2 Citations (Scopus)

Abstract

Analyzing online consumer reviews is one of many popular applications of natural language processing in retail sectors. Text embedding models can transform the textual content of reviews into numerical representations for downstream analytic tasks and therefore they play an essential role in review analytics. As review analytics is increasingly used in the industries, more empirical research is needed to investigate how text embeddings perform in understanding the thoughts and attitudes of customers. In this study, we examined four commonly used text embeddings: namely TF-IDF, word2vec, sent2vec and BERT, to evaluate their performance in predicting the ratings and the sentiments of online consumer reviews. Drawn on the results, we highlight the strengths of these text embeddings and their desirable use cases. Our findings reveal that BERT and sent2vec can produce stable results in predicting the ratings of retail reviews in general. Besides, word2vec is more suitable for identifying negative sentiment within reviews. From a practical perspective, it is worth analyzing reviews from different product categories separately to achieve better results.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science, ICIS 2022
EditorsZheng'an Yao, Simon Xu, Jixin Ma, Wencai Du, Wei Lui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-175
Number of pages6
ISBN (Electronic)9781665494632
DOIs
Publication statusPublished - 2022
Event22nd IEEE/ACIS International Conference on Computer and Information Science, ICIS 2022 - Zhuhai, China
Duration: 26 Jun 202228 Jun 2022

Publication series

NameProceedings - 2022 IEEE/ACIS 22nd International Conference on Computer and Information Science, ICIS 2022

Conference

Conference22nd IEEE/ACIS International Conference on Computer and Information Science, ICIS 2022
Country/TerritoryChina
CityZhuhai
Period26/06/2228/06/22

Keywords

  • business analytics
  • online consumer review
  • retail
  • text embedding

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