Micro-Blog Fine-Grained Sentiment Analysis Based on Multi-Feature Fusion
[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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