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Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning

Published: 23 August 2023 Publication History

Abstract

The text mining of online reviews is currently a popular research direction of e-commerce and is considered the next blue ocean. Online reviews can dig out consumer preferences and provide theoretical guidance for the improvement of product features. However, current research mostly focuses on sentiment analysis methods and rarely involves feature extraction and large-scale data recognition. This article uses word segmentation technology to create a new feature extraction method. With the long short-term memory neural network and latent Dirichlet allocation topic model, we propose a product feature improvement model—CESC (Consumer online reviews–Extract short text–Sentiment analysis–Cluster feature). The model can derive the product features and attitudes that consumers prefer based on consumer online reviews and use it to improve product features. According to the experimental results of three electronic products sold on the e-commerce platform, the model can effectively dig out consumer preferences for online reviews. Enterprises can improve the quality of products and services, better meet the needs of consumers, promote consumers’ consumption, and achieve the enterprises’ goals and values.

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  1. Online Reviews Sentiment Analysis and Product Feature Improvement with Deep Learning

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    Published In

    cover image ACM Transactions on Asian and Low-Resource Language Information Processing
    ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 22, Issue 8
    August 2023
    373 pages
    ISSN:2375-4699
    EISSN:2375-4702
    DOI:10.1145/3615980
    Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 August 2023
    Online AM: 15 March 2022
    Accepted: 23 February 2022
    Revised: 06 February 2022
    Received: 31 October 2021
    Published in TALLIP Volume 22, Issue 8

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    Author Tags

    1. Online reviews
    2. text mining
    3. sentiment analysis
    4. product feature improvement
    5. deep learning
    6. consumer prefer

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    • Natural Science Foundation of Hebei Province, China

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