research-article

Text Sentiment Analysis Based on CNN-BiGRU Enhanced Features

Authors:

Haiming LiAuthors Info & Claims

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

Pages 15 - 21

https://doi.org/10.1145/3582197.3582200

Published: 30 March 2023 Publication History

Abstract

In order to solve the problem that traditional neural network models tend to ignore the importance between aspect words and context, and can not fully obtain and utilize the corresponding features in aspect sentiment analysis task.A neural network model of convolutional neural network(CNN) and bidirectional gated recurrent unit(BiGRU) based on attention mechanism to enhance the aspect words features is proposed.The model enhances feature information related to aspect words through CNN and BiGRU.Then The enhanced features are used by BiGRU to obtain global features, and finally the attention mechanism is used to screen out the most relevant features of the global features and get the emotion category of the text.Experimental results show that compared with other baseline models, this model can obtain more relevant aspect features.The accuracy and F1 values are better than other models.

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Cited By

Liang S(2024)Enhancing Multimodal Emotional Information Extraction in Film and Television through Adaptive Feature Fusion with DenseNe, Transformer, and 3D CNN ModelsApplied Artificial Intelligence10.1080/08839514.2024.241960938:1Online publication date: 26-Oct-2024
https://doi.org/10.1080/08839514.2024.2419609

Index Terms

Text Sentiment Analysis Based on CNN-BiGRU Enhanced Features
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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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.

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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

Liang S(2024)Enhancing Multimodal Emotional Information Extraction in Film and Television through Adaptive Feature Fusion with DenseNe, Transformer, and 3D CNN ModelsApplied Artificial Intelligence10.1080/08839514.2024.241960938:1Online publication date: 26-Oct-2024
https://doi.org/10.1080/08839514.2024.2419609

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