Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis

Abstract Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts \textit{positive} when seeing the word \textit{disappointed}. Meanwhile, it is less stressed that attention mechanism is likely to {``}over-focus{''} on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader {``}focus{''} on different parts of the sentence. The experimental results demonstrate the effectiveness of our approach.