skip to main content
10.1145/3448734.3450795acmotherconferencesArticle/Chapter ViewAbstractPublication PagesdtmisConference Proceedingsconference-collections
research-article

Sentiment Analysis of Chinese Short Text Based on Multiple Features

Published: 17 May 2021 Publication History

Abstract

With the rapid development of mobile internet and the increase of social platforms, higher performance requirements are put forward for sentiment analysis of Chinese short texts. The traditional deep learning models based on CNN, LSTM and other frameworks face the problem of unable to extract all the effective information contained in the text because of the single direction of text parsing. However, complex model which combines framework in serial way exist a problem that cannot be ignored, that is it can not get effective training. To solve the above problems, this paper proposes an interpretable emotion analysis framework MIX-CNN-BiLSTM-Attention-Transformer (MCBAT Model), which can extract different features from multiple models. From the three dimensions of the fixed collocation of words, the context information and the importance of words in the text, CNN, BiLSTM-Attention and Transformer model are used to extract the above three different features. After vector splicing, the classification results are obtained by the classifier through the full connection layer. The accuracy and stability of the MCBAT model are improved compared with other classical emotion analysis models (CNN, BiLSTM, CNN-BiLSTM, etc.) and LSTM-LDA model. The model based on multi feature consideration is of great significance to emotion analysis task, and provides a method support for further development in the future.

References

[1]
Zhang Yun tao, Gong Ling, and Wang Yong- cheng, “An improved TF-IDF approach for text classification,” Journal of Zhejiang University SCIENCE A, vol. 6, pp. 9-55, 2005.
[2]
WANG Haitao, HE Jie, ZHANG Xiaohong, and LIU Shufen, “A Short Text Classification Method Based on N-Gram and CNN,” Chinese Journal of Electronics, vol. 29, no. (02), pp. 248-254, 2020.
[3]
Huang Chunmei, and Wang Songlei, “Short text classification based on bag of words model and TF-IDF,” Software engineering, vol. 23, no.(03), pp. 1-3, 2020.
[4]
Zhang Lihu's method of emotion classification based on Zhang Lihu, J. 2020.
[5]
P.H. Chen, C.J. Lin, and B. Schölkopf, “A tutorial on v support vector machines,” Appl. Stoch. Models. Bus. Ind. vol. 21, pp. 111-136, 2005.
[6]
Song Tingyong, “Semantic based fuzzy spectral clustering of Chinese short texts,” East China Normal University, 2015.
[7]
Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E., “Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pp. 1097-1105, 2012.
[8]
Tang D, Qin B, and Liu T., “Document modeling with gated recurrent neural network for sentiment classification,” Proceedings of the 2015 conference on empirical methods in natural language processing, pp. 1422-1432, 2015.
[9]
Vaswani A, Shazeer N, Parmar N, “Attention is all you need,” Proc of the 31st Conference on Neural Information Processing System, pp. 344-351, 2017.
[10]
Yoon Kim, “Convolutional Neural Networks for Sentence,” Computer Science: Computation and Language, pp. 1-6, 2014.
[11]
Lu C, Huang H, Jian P, “A P-LSTMneural network for sentiment classification,” Computer Science, no. (10234), pp. 524-533, 2017.
[12]
Volodymyr Mnih, Nicolas Heess, Alex Graves, and Koray Kavukcuoglu, “Recurrent Models of Visual Attention,” Computer Vision and Pattern Recognition, pp. 1406-6247, 2014.
[13]
Bahdanau D, Cho K, and Bengio Y., “Neural Machine Translation by Jointly Learning to Align and Translate,” Computer ence, pp, 1409-0473, 2014.
[14]
Wang Wei, Sun Yuxia, Qi Qingjie, and Meng Xiangfu, “Text sentiment classification model based on bigru attention neural network,” Computer application research, vol. 36, no. (12), pp. 3558-3564, 2019.
[15]
Zhang Y, Yuan H, Wang J, “Using a CNN-LSTM model for sentiment intensity prediction,” Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 200-204, 2017.
[16]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, “Efficient Estimation of Word Representations in Vector Space,” Computation and Language, pp. 1301-3781, 2013.
[17]
Chen Wenshi, Liu Xinhui, and Lu Mingyu, “Deep topic feature extraction for multi label text classification,” Pattern recognition and artificial intelligence, vol. 32, no. (09), pp. 785-79, 2019.

Cited By

View all
  • (2023)Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classificationPLOS ONE10.1371/journal.pone.029258218:10(e0292582)Online publication date: 12-Oct-2023
  • (2022)Text-Based Emotion Detection using CNN-BiLSTM2022 4th International Conference on Cybernetics and Intelligent System (ICORIS)10.1109/ICORIS56080.2022.10031370(1-5)Online publication date: 8-Oct-2022

Index Terms

  1. Sentiment Analysis of Chinese Short Text Based on Multiple Features
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        CONF-CDS 2021: The 2nd International Conference on Computing and Data Science
        January 2021
        1142 pages
        ISBN:9781450389570
        DOI:10.1145/3448734
        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 ACM 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]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 17 May 2021

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. Sentiment analysis
        2. deep learning
        3. hybrid model framework
        4. short Chinese text

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Conference

        CONF-CDS 2021

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • Downloads (Last 12 months)10
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 09 Jan 2025

        Other Metrics

        Citations

        Cited By

        View all
        • (2023)Research on performance variations of classifiers with the influence of pre-processing methods for Chinese short text classificationPLOS ONE10.1371/journal.pone.029258218:10(e0292582)Online publication date: 12-Oct-2023
        • (2022)Text-Based Emotion Detection using CNN-BiLSTM2022 4th International Conference on Cybernetics and Intelligent System (ICORIS)10.1109/ICORIS56080.2022.10031370(1-5)Online publication date: 8-Oct-2022

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media