research-article

Performance Comparison of Deep Learning Text Embeddings in Sentiment Analysis Tasks with Online Consumer Reviews

Authors:

Patrick Cheong-Iao Pang,

Ho Yin KanAuthors Info & Claims

ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City

Pages 1 - 7

https://doi.org/10.1145/3582197.3582198

Published: 30 March 2023 Publication History

Abstract

In order to investigate the effect of various natural language processing models on different data processing, this paper adopted the consumer reviews of two well-known Internet retailing websites: Yelp and Zappos, and used four text embedding methods: word2vec, Glove, BERT, and GPT-2 and two text classification methods: SVM and Neural Network (NN) for text classification, in order to compare the performance of the combinations of these text mining techniques. The result shows that BERT is the best-performing text embedding method overall in both datasets when used with both SVM and NN. It is also found that NN is better than SVM for overall text classification. As an exploratory experiment, we aim to provide a three-dimensional comparison to find the most suitable algorithm for consumer review data, and the implication is that BERT and NN can achieve satisfactory results in most of the scenarios.

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Cited By

Malik NBilal M(2024)Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challengesPeerJ Computer Science10.7717/peerj-cs.220310(e2203)Online publication date: 19-Jul-2024
https://doi.org/10.7717/peerj-cs.2203
Guo SDuan YHuang JZheng DPang PTong H(2023)BBPM: A Study of Information Pre-retrieval Models Based on Medical BERT Model2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507322(2389-2393)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICCC59590.2023.10507322

Index Terms

Performance Comparison of Deep Learning Text Embeddings in Sentiment Analysis Tasks with Online Consumer Reviews
1. Applied computing
  1. Electronic commerce
    1. Online shopping
2. Information systems
  1. World Wide Web
    1. Web mining
      1. Data extraction and integration

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ICIT '22: Proceedings of the 2022 10th International Conference on Information Technology: IoT and Smart City

December 2022

385 pages

ISBN:9781450397438

DOI:10.1145/3582197

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 30 March 2023

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ICIT 2022

ICIT 2022: IoT and Smart City

December 23 - 25, 2022

Shanghai, China

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Cited By

Malik NBilal M(2024)Natural language processing for analyzing online customer reviews: a survey, taxonomy, and open research challengesPeerJ Computer Science10.7717/peerj-cs.220310(e2203)Online publication date: 19-Jul-2024
https://doi.org/10.7717/peerj-cs.2203
Guo SDuan YHuang JZheng DPang PTong H(2023)BBPM: A Study of Information Pre-retrieval Models Based on Medical BERT Model2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507322(2389-2393)Online publication date: 8-Dec-2023
https://doi.org/10.1109/ICCC59590.2023.10507322

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