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Text Sentiment Analysis Method Based on Support Vector Machine And Long Short-term Memory Network

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Lepeng WangAuthors Info & Claims

CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

Pages 87 - 91

https://doi.org/10.1145/3603781.3603796

Published: 27 July 2023 Publication History

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Abstract

Machine learning is a hot technology today and plays a pivotal role in text sentiment analysis [1]. Text has complex properties such as semantic word order grammar and contextual relationship, so the accuracy of text sentiment analysis faces significant challenges. There are some classic methods in the industry for text sentiment analysis, such as Support Vector Machines (SVM) and Naive Bayes[2]. These methods are strongly related to feature extraction, with high complexity and average performance. With the development of neural network technology, people began to use neural network models for text sentiment analysis, but compared with traditional methods, neural network processing corpus is more accurate, but slower. Therefore, this paper adopts the method of combining classical algorithm model and neural network model for text sentiment analysis, which can improve the processing efficiency without changing the accuracy.

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Ho CYue QLim T(2024)Investigating The Role of Paraphrasing in Sentiment AnalysisProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654529(36-41)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654529

Index Terms

Text Sentiment Analysis Method Based on Support Vector Machine And Long Short-term Memory Network
1. Applied computing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Machine learning approaches

Index terms have been assigned to the content through auto-classification.

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CNIOT '23: Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things

May 2023

1025 pages

ISBN:9798400700705

DOI:10.1145/3603781

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Association for Computing Machinery

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Published: 27 July 2023

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CNIOT'23

CNIOT'23: 2023 4th International Conference on Computing, Networks and Internet of Things

May 26 - 28, 2023

Xiamen, China

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Ho CYue QLim T(2024)Investigating The Role of Paraphrasing in Sentiment AnalysisProceedings of the 2024 9th International Conference on Intelligent Information Technology10.1145/3654522.3654529(36-41)Online publication date: 23-Feb-2024
https://dl.acm.org/doi/10.1145/3654522.3654529

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Network text sentiment analysis method combining LDA text representation and GRU-CNN

Wavelet twin support vector machine

Twin support vector machine: theory, algorithm and applications

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