Aspect-Based Sentiment Analysis Using Word Embedding Restricted Boltzmann Machines

Recent years, many studies have addressed problems in sentiment analysis at different levels, and building aspect-based methods has become a central issue for deep opinion mining. However, previous studies need to use two separated modules in order to extract aspect-sentiment word pairs, then predict the sentiment polarity. In this paper, we use Restricted Boltzmann Machines in combination with Word Embedding model to build the joined model which not only extracts aspect terms appeared and classifies them into respective categories, but also completes the sentiment polarity prediction task. The experimental results show that the method we use in aspect-based sentiment analysis tasks is better than other state-of-the-art approaches.