A Proposal for Book Oriented Aspect Based Sentiment Analysis: Comparison over Domains

Aspect-based sentiment analysis (absa) deals with extracting opinions at a fine-grained level from texts, providing a very useful information for companies which want to know what people think about them or their products. Most of the systems developed in this field are based on supervised machine learning techniques and need a high amount of annotated data, nevertheless not many resources can be found due to their high cost of preparation. In this paper we present an analysis of a recently published dataset, covering different subtasks, which are aspect extraction, category detection, and sentiment analysis. It contains book reviews published in Amazon, which is a new domain of application in absa literature. The annotation process and its characteristics are described, as well as a comparison with other datasets. This paper focuses on this comparison, addressing the different subtasks and analyzing their performance and properties.