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Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance

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Abstract

In sentiment analysis of microblogs that involve social relationships, a common approach is to expand the features of target microblogs using microblog relationship networks. However, the current research methodology only relies on individual interaction behaviors on social platforms to construct these networks, disregarding the guiding influence of microblog relationship networks on microblogs. Consequently, this leads to the feature expansion of microblogs while introducing interference among them. To address this problem, this study aims to construct a more precise microblog relationship network by incorporating multiple interactive behaviors from social platforms. This network will serve as a guide for sentiment interactions among microblog texts, thereby mitigating feature interference. Firstly, we utilize various interaction behaviors on social platforms to build a microblog relationship network. We employ a LINE network embedding to represent the microblog relationship network as microblog relationship features. Secondly, we extract word-level and sentence-level features from the microblog text using a BERT pre-training model. The word-level features are combined using convolutional neural networks. Subsequently, the word-level and sentence-level features of microblogs are separately guided for interaction fusion through relational features. An attention network is then constructed to fuse the post-interaction features in a single step. Prior to secondary fusion, the primary fusion features, post-interaction word-level features, and post-interaction sentence-level features are weighted, and the sentiment categories of microblogs are outputted. Finally, we compare the proposed method with the text-only microblog sentiment analysis approach and the sentiment analysis method that incorporates social relationships on two real datasets. The comparison results demonstrate the superiority of our proposed method.

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Data Availability

All the datasets are publicly available and can be obtained using the cited sources.

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Funding

This work was supported by the National Natural Science Foundation of China (Nos. 61903056 and 61702066) and the Chongqing Research Program of Basic Research and Frontier Technology (Nos. cstc2019jcyj-msxmX0681 and cstc2021jcyj-msxmX0761). This work was partially supported by Project PID2020-119478GB-I00 funded by MICINN/AEI/10.13039/501100011033.

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Authors and Affiliations

  1. School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Chenquan Gan & Qingyi Zhu

  2. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, 400065, Chongqing, China

    Xiaopeng Cao

  3. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, 116024, Dalian, China

    Deepak Kumar Jain

  4. Symbiosis Institute of Technology, Symbiosis International University, 412115, Pune, India

    Deepak Kumar Jain

  5. Department of Computer Science and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence, University of Granada, 18071, Granada, Spain

    Salvador García

Authors
  1. Chenquan Gan
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  2. Xiaopeng Cao
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  3. Qingyi Zhu
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  4. Deepak Kumar Jain
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  5. Salvador García
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Contributions

Conceptualization, Methodology, Formal analysis and investigation, Writing - original draft preparation: Chenquan Gan, Xiaopeng Cao; Writing - review and editing: Qingyi Zhu; Writing - review and editing, Supervision: Deepak Kumar Jain and Salvador García.

Corresponding author

Correspondence to Salvador García.

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Gan, C., Cao, X., Zhu, Q. et al. Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance. Appl Intell 54, 443–459 (2024). https://doi.org/10.1007/s10489-023-05206-y

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