Aspect Extraction from Reviews using Convolutional Neural Networks and Embeddings ⋆ Peiman Barnaghi, Georgios Kontonatsios, Nik Bessis, and Yannis Korkontzelos Edge Hill University, Liverpool, United Kingdom {barnaghp, Georgios.Kontonatsios, Nik.Bessis, Yannis.Korkontzelos}@edgehill.ac.uk Abstract. Aspect-based sentiment analysis is an important natural lan- guage 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 shortcom- ings 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 per- forms comparably well or better than more complex systems that may take longer to train. Keywords: Aspect-based sentiment analysis · Aspect extraction · Con- volutional Neural Networks · Deep learning· NLP 1 Introduction Currently immense volumes of text-based reviews are available, in a great va- riety of diverse domains. Consumers can share their experience on services and products. Natural Language Processing (NLP) methods can be used to extract meaningful information from this data. Quantifying sentiment expressed for vari- ous aspects of a product or service can help producers and consumers to monitor, assess and make decisions. Significant volume of research has focused on Exten- sive research has focussed on analysing online reviews for a variety of topics or products, e.g. movies, restaurants, mobile applications and software projects. Aspect-based sentiment analysis is a variation of sentiment analysis that considers different aspects of the object of a text-based review and classifies the comments for each aspect as positive, negative or neutral. For example, in the comment “the food is great but expensive and service is slow” three ⋆ The final authenticated version is available online at https://doi.org/10.1007/978-3- 030-23281-8 37.