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- W3091285223 abstract "Given certain data available for training, the keys to improving a sentiment analysis system lie in developing a good model that is capable of capturing both local and global features of texts, as well as incorporating external knowledge into the model effectively. In this paper, we propose a multi-window Convolutional Transformer (ConvTransformer) that takes the advantages of both Transformer and CNN for sentiment analysis. The proposed ConvTransformer is able to capture important local n-gram features effectively while preserving sequential information of texts. Furthermore, we propose a sentiment-aware attention mechanism to incorporate the sentiment intensity information of each word by utilizing an external knowledge base, SentiWordNet. The sentiment-aware attention mechanism takes both sentiment and position information of each token into consideration when computing attention weights, resulting in a global feature for final classification. Comparing with CNN, RNN and attention-based baseline models, our model achieves the best performance on multiple sentiment analysis datasets." @default.
- W3091285223 created "2020-10-08" @default.
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- W3091285223 date "2020-07-01" @default.
- W3091285223 modified "2023-10-16" @default.
- W3091285223 title "Convolutional Transformer with Sentiment-aware Attention for Sentiment Analysis" @default.
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- W3091285223 doi "https://doi.org/10.1109/ijcnn48605.2020.9206796" @default.
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