Aspect Level Sentiment Classification with Memory Network Using Word Sentiment Vectors and a New Attention Mechanism AM-PPOSC

摘要

For aspect level sentiment classification, the attention mechanisms of memory network can capture the importance of each context word when inferring the sentiment polarity of an aspect. However, the existing attention mechanisms of memory network are either content-based or location-based while the part of speech (POS) of word is not used. Accordingly, this paper proposes a new attention mechanism called AM-PPOSC which uses the location, POS and content information. In addition, the common-used algorithms for learning word embedding typically only model the syntactic context of words but ignore the sentiment polarities of words. So, this paper proposes a method that based on TD-LSTM and SentiWordNet 3.0 to capture the sentiment polarities of words which represented as word sentiment vectors. Then, AM-PPOSC and word sentiment vectors are applied to memory network. Experiments on two datasets demonstrate that not only AM-PPOSC outperforms the original attention mechanisms of memory network when inferring the sentiment polarity of an aspect, but also word sentiment vectors can improve the performance of memory network. Furthermore, compared with feature-based SVM, LSTM and TD-LSTM, memory network with AM-PPOSC and word sentiment vectors achieves higher accuracies on two datasets.