Attention-based LSTM for Aspect-level Sentiment Classification

The architecture of ATAE-LSTM

摘要

Aspect-level sentiment classification is a fine-grained task in sentiment analysis. Since it provides more complete and in-depth results, aspect-level sentiment analysis has received much attention these years. In this paper, we reveal that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect. For instance, “The appetizers are ok, but the service is slow.”, for aspect taste, the polarity is positive while for service, the polarity is negative. Therefore, it is worthwhile to explore the connection between an aspect and the content of a sentence. To this end, we propose an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification. The attention mechanism can concentrate on different parts of a sentence when different aspects are taken as input. We experiment on the SemEval 2014 dataset and results show that our model achieves state-of-the-art performance on aspect-level sentiment classification.

出版物
In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Cited by 2,000+ until Mar. 2023.

Yequan Wang
Yequan Wang
研究员,团队主管

我的研究兴趣包含大模型,具身智能和自然语言处理等。