Aspect-Based Sentiment Analysis with Position Embedding Interactive Attention Network
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초록
Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generatednatural language. So far, most of the methods only use the implicit position information of the aspect in thecontext, instead of directly utilizing the position relationship between the aspect and the sentiment terms. Infact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of givenaspects, and proposes a position embedding interactive attention network based on a long short-term memorynetwork. Firstly, it uses the position information of the context simultaneously in the input layer and theattention layer. Secondly, it mines the importance of different context words for the aspect with the interactiveattention mechanism. Finally, it generates a valid representation of the aspect and the context for sentimentclassification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurantdataset and 1% on the laptop dataset.
Abstract
Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generatednatural language. So far, most of the methods only use the implicit position information of the aspect in thecontext, instead of directly utilizing the position relationship between the aspect and the sentiment terms. Infact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of givenaspects, and proposes a position embedding interactive attention network based on a long short-term memorynetwork. Firstly, it uses the position information of the context simultaneously in the input layer and theattention layer. Secondly, it mines the importance of different context words for the aspect with the interactiveattention mechanism. Finally, it generates a valid representation of the aspect and the context for sentimentclassification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurantdataset and 1% on the laptop dataset.
참고문헌 (29)
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