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| Micro-Blog Fine-Grained Sentiment Analysis Based on Multi-Feature Fusion |
Wu Xuxu1,Chen Peng1( ),Jiang Huan2 |
1School of Information and Cyber Security, People’s Public Security University of China, Beijing 100045, China
2School of E-Business and Logistics, Beijing Technology and Business University, Beijing 100048, China |
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Abstract [Objective] This paper proposes an RB-LCM model to improve the fine-grained sentiment analysis of Weibo texts. [Methods] First, we used the RoBERTa to encode the character and sentence-level features of Weibo posts. Then, we utilized the Bi-LSTM and capsule network to capture in-depth global and local features of Weibo sentences. Third, we deployed multi-head self-attention feature fusion to fuse the relevant multi-dimensional features. Finally, we used improved Focal Loss and FGM to train the model and improve the dataset labels’ imbalance and the model’s robustness. [Results] The accuracy and F1 value of the proposed model on the SMP2020-EWECT dataset reached 80.64% and 77.41%. The model’s accuracy and F1 value on the NLPCC2013 task 2 dataset were 67.17% and 51.08%. The model’s accuracy and F1 value on the NLPCC2014 task 1 dataset reached 71.27% and 58.25%. The model’s accuracy and F1 value on the binary sentiment dataset weibo_senti_100k dataset were up to 98.45% and 98.44%, respectively. All results were better than the advanced sentiment analysis models on each dataset. [Limitations] Our model did not include relevant pictures, videos, voice, or other information for sentiment analysis. [Conclusions] The proposed model can effectively analyze the sentiment of Weibo posts.
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Received: 28 September 2022
Published: 13 September 2023
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| Fund:Fundamental Research Funds for the Central Universities, People’s Public Security University of China Project(2022JKF02018) |
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Corresponding Authors:
Chen Peng，E-mail：chenpeng@ppsuc.edu.cn。
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