Abstract
Aspect level sentiment classification is a fundamental task in the field of sentiment analysis, which goal is to inferring sentiment on entities mentioned within texts or aspects of them. Since it performs finer-grained analysis, aspect level sentiment classification is more challenging. Recently, neural network approaches, such as LSTMs, have achieved much progress in sentiment analysis. However, most neural models capture little aspect information in sentences. Aspect level sentiment of a sentence is determined not only by the content but also by the concerned aspect. In this paper, we propose a novel LSTM with Aspect Attention model (LSTM_AA) for aspect level sentiment classification. Our model introduces aspect attention to relate the aspect level sentiment of a sentence closely to the concerned aspect, as well as to explore the connection between an aspect and the content of a sentence. We experiment on the SemEval 2014 datasets and results show that our model performs comparable to state-of-the-art deep memory network, and substantially better than other neural network approaches. Besides, our approach is more robust than deep memory network which performance heavily depends on the hops.
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Li, C., Wang, H., He, S. (2019). Aspect Level Sentiment Analysis with Aspect Attention. In: U., L., Lauw, H. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11607. Springer, Cham. https://doi.org/10.1007/978-3-030-26142-9_22
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