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A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models

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Abstract  

In addition to a large amount of text, there are also many emoticons in the comment data on social media platforms. The multimodal nature of online comment data increases the difficulty of sentiment analysis. A big data sentiment analysis technology for social online multimodal (SOM) comments has been proposed. This technology uses web scraping technology to obtain SOM comment big data from the internet, including text data and emoji data, and then extracts and segments the text big data, preprocess part of speech tagging. Using the attention mechanism-based feature extraction method for big SOM comment data and the correlation based expression feature extraction method for SOM comment, the emotional features of SOM comment text and expression package data were obtained, respectively. Using the extracted two emotional features as inputs and the ELMO pre-training model as the basis, a GE-Bi LSTM model for SOM comment sentiment analysis is established. This model combines the ELMO pre training model with the Glove model to obtain the emotional factors of social multimodal big data. After recombining them, the GE-Bi LSTM model output layer is used to output the sentiment analysis of big SOM comment data. The experiment shows that this technology has strong extraction and segmentation capabilities for SOM comment text data, which can effectively extract emotional features contained in text data and emoji packet data, and obtain accurate emotional analysis results for big SOM comment data.

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Data will be made available on reasonable request.

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Acknowledgements

This work is supported by Key Project of Science and Technology Research of Chongqing Education Commission with No.KJZD-K202203902. The authors also acknowledge contributions to this project from the Rector of the Silesian University of Technology, Poland under a proquality grant no. 09/010/RGJ24/0031.

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Authors and Affiliations

  1. Mahasarakham University, Mahasarakham, Thailand

    Jun Wan

  2. ChongQing City Vocational College, ChongQing, China

    Jun Wan

  3. Faculty of Applied Mathematics, Silesian University of Technology, Gliwice, Poland

    Marcin Woźniak

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  1. Jun Wan
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  2. Marcin Woźniak
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Contributions

Jun Wan contributed to Writing—Original Draft, Methodology, and Conceptualization; Marcin Woźniak contributed to Conceptualization and Writing—Review and Editing.

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Correspondence to Marcin Woźniak.

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Wan, J., Woźniak, M. A Sentiment Analysis Method for Big Social Online Multimodal Comments Based on Pre-trained Models. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02303-1

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