I. Introduction
Sentiment analysis has been one of the most active fields of research in Natural Language Processing (NLP) [1]–[5]. It has been researched as a response to the growing availability of opinion-rich resources, such as personal blogs and online review sites [6]. Rapid development can also be attributed to high business demand for user feedback. One of such use cases is processing user reviews, for instance, in ecommerce systems. In most cases, sentiment analysis focuses on inferring the sentiment of the entire review or sentence. However, it is not always the optimal choice since one review or sentence might contain information on both positive and negative aspects of a product or service. A user might like the quality of a product or service but loathe the price. The Aspect Based Sentiment Analysis (ABSA) is proposed as a method to extract user attitudes towards specific aspects of a product or service. This approach allows inferring more indepth sentiment, which could be invaluable for business as it could, e.g., allow a company to focus on refining negative aspects of their products or services while maintaining aspects valued by their users. ABSA focuses on the relationship between three clearly defined elements – a subject, an aspect of the subject, and a sentiment expressed towards subject and aspect.