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| Weibo Comments Sentiment Classification Based on BERT and Text CNN |
| XU Kaixuana, LI Xianb, PAN Yaleia
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| a. Institute of Complexity Science; b. Institude For Future,Qingdao University, Qingdao 266071, China |
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Abstract For comments with multiple sections within sentences, some state-of-art models, such as Embedding from Language Models-Text Convolutional Neural Network and Generative Pre-trained Transformer model, cannot accurately extract the meaning and therefore result in unsatisfactory performance. To solve this problem, we utilize Bidirectional Encoder Representations from Transformers-Text Convolutional Neural Network and Generative Pre-trained Transformer model. Using the bidirectional code converter structure of BERT′s unique self-attention mechanism, we can obtain the word vector of the global feature of the sentence, then we input the word vectors into Text CNN, then using Text CNN to capture local features,finally we extract high-level features, such as semantics and contextual connection. This process solved the problem of inaccurate contextual connection of the text obtained by the model, allowing us to realize the fine-grained sentiment classification of Weibo comments with high accuracy. Meanwhile, to verify the advantages of the model, we compared it with existing models. The test results on the simplifyweibo_4_moods dataset show that the BERT-Text CNN model has improved accuracy, recall, and F1 indicators.
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Received: 02 November 2020
Published: 10 May 2021
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