Aspect based Sentiment Analysis with Feature Enhanced Attention CNN-BiLSTM

Previous work has recognized the importance of using the attention mechanism to obtain the interaction between aspect words and contexts for sentiment analysis. However, for the most attention mechanisms, it is unrigorous to use the average vector of the aspect words to calculate the context attention. Besides, the feature extraction ability of the model is also essential for effective analysis, the combination of CNN and LSTM can enhance the feature extraction ability and semantic expression ability of the model, which is also a popular research trend. This paper introduces an aspect level neural network for sentiment analysis named Feature Enhanced Attention CNN-BiLSTM (FEA-NN). Our method is to extract a higher-level phrase representation sequence from the embedding layer by using CNN, which provides effective support for subsequent coding tasks. In order to improve the quality of context encoding and preserve semantic information, we use BiLSTM to capture both local features of phrases as well as global and temporal sentence semantics. Besides, we add an attention mechanism to model interaction relationships between aspect words and sentences to focus on those keywords of targets to learn more effective context representation. We evaluate the proposed model on three datasets: Restaurant, Laptop, and Twitter. Extensive experiments show that the effectivess of FEA-NN.