Aspect Extraction from Reviews using Convolutional Neural Networks and Embeddings

Aspect-based sentiment analysis is an important natural language processing task that allows to extract the sentiment expressed in a review for parts or aspects of a product or service. Extracting all aspects for a domain without manual rules or annotations is a major challenge. In this paper, we propose a method for this task based on a Convolutional Neural Network (CNN) and two embedding layers. We address shortcomings of state-of-the-art methods by combining a CNN with an embedding layer trained on the general domain and one trained the specific domain of the reviews to be analysed. We evaluated our system on two SemEval datasets and compared against state-of-the-art methods that have been evaluated on the same data. The results indicate that our system performs comparably well or better than more complex systems that may take longer to train.