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Text tendency analysis based on multi-granularity emotional chunks and integrated learning

  • S. I : Intelligent Computing Methodologies in Machine learning for IoT Applications
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Abstract

Internet is a new type of information exchange tool that has developed with the times. Now, it has been integrated into all aspects of our study and life. At the same time, in the era of the rise of social media, there are more and more platforms on the Internet. A variety of critical texts have also exploded in these platforms. In these opinions and comments, the subjective opinions of the presenters are included, and the emotional tendencies of the commenters are expressed. Nowadays, subjective text information resources are huge. One of the major problems to be solved by management objects facing information management is how to manage them effectively so that users can quickly and accurately find the required information. Therefore, classifying these text tendencies and mining the potential value in the text has broad application prospects. Based on the text granularity and processing efficiency, this paper conducts multi-granularity emotional block partitioning on network texts and compares the sentiment analysis under different granularities. Furthermore, a random subspace integrated learning text sentiment classification method based on BPSO (binary particle swarm optimization) is proposed to analyze text orientation. By simulating news site comments and e-commerce website reviews such as Taobao, the convergence analysis of BPSO in the optimization of the number of base classifiers shows that the BPSO algorithm can be applied to the random subspace method well, and the accuracy of the classification results is high.

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Acknowledgements

This work has been supported by the State Key Program of National Nature Science Foundation of China (61936001), the National Key Research and Development Program of China under Grants 2016QY01W0200, the National Natural Science Foundation of China under Grants 61772096 and 61533020, and the talent training project for Ph.D. student of Chongqing University of posts and Telecommunications under Grants BYJS201913.

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Haichao Sun, Guoyin Wang & Shuyin Xia

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  1. Haichao Sun
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  2. Guoyin Wang
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  3. Shuyin Xia
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Correspondence to Guoyin Wang.

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Sun, H., Wang, G. & Xia, S. Text tendency analysis based on multi-granularity emotional chunks and integrated learning. Neural Comput & Applic 33, 8119–8129 (2021). https://doi.org/10.1007/s00521-020-04901-y

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