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- W3165519921 abstract "Many existing attention-based deep learning approaches to sentiment analysis have focused on words and represent an entire review text as a word sequence. However, these approaches overlook the differences in the importance of each sentence to the complete text. To solve this problem, some work has been performed to calculate sentence-level attention, but these studies use the same approach that is applied to word-level attention, which leads to unnecessary sequential structures and increased complexity of sentence representation. Therefore, in this paper, we propose a sentence-to-sentence attention network1 (S2SAN) using multihead self-attention. We conducted several domain-specific, cross-domain and multidomain sentiment analysis experiments with real-world datasets. The experimental results show that S2SAN outperforms other state-of-the-art models. Some classical sentiment classifiers [e.g., convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) models] achieve better accuracies when they are reconfigured to include sentence-to-sentence attention." @default.
- W3165519921 created "2021-06-07" @default.
- W3165519921 creator A5013736647 @default.
- W3165519921 creator A5042450827 @default.
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- W3165519921 date "2021-10-01" @default.
- W3165519921 modified "2023-10-17" @default.
- W3165519921 title "S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews" @default.
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- W3165519921 doi "https://doi.org/10.1016/j.dss.2021.113603" @default.
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