Microblog Sentiment Analysis Based on Dynamic Character-Level and Word-Level Features and Multi-Head Self-Attention Pooling

Shangyi Yan, Jingya Wang and Zhiqiang Song
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Shangyi Yan: College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
Jingya Wang: College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
Zhiqiang Song: College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China

Future Internet, 2022, vol. 14, issue 8, 1-19

Abstract: To address the shortcomings of existing deep learning models and the characteristics of microblog speech, we propose the DCCMM model to improve the effectiveness of microblog sentiment analysis. The model employs WOBERT Plus and ALBERT to dynamically encode character-level text and word-level text, respectively. Then, a convolution operation is used to extract local key features, while cross-channel feature fusion and multi-head self-attention pooling operations are used to extract global semantic information and filter out key data, before using the multi-granularity feature interaction fusion operation to effectively fuse character-level and word-level semantic information. Finally, the Softmax function is used to output the results. On the weibo_senti_100k dataset, the accuracy and F1 values of the DCCMM model improve by 0.84% and 1.01%, respectively, compared to the best-performing comparison model. On the SMP2020-EWECT dataset, the accuracy and F1 values of the DCCMM model improve by 1.22% and 1.80%, respectively, compared with the experimental results of the best-performing comparison model. The results showed that DCCMM outperforms existing advanced sentiment analysis models.

Keywords: dynamic character and word encoding; multi-head self-attention pooling; multi-granularity feature interactive fusion; microblog sentiment analysis (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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