Survey on aspect detection for aspect-based sentiment analysis

Abstract

Sentiment analysis is an important tool to automatically understand the user-generated content on the Web. The most fine-grained sentiment analysis is concerned with the extraction and sentiment classification of aspects and has been extensively studied in recent years. In this work, we provide an overview of the first step in aspect-based sentiment analysis that assumes the extraction of opinion targets or aspects. We define a taxonomy for the extraction of aspects and present the most relevant works accordingly, with a focus on the most recent state-of-the-art methods. The three main classes we use to classify the methods designed for the detection of aspects are pattern-based, machine learning, and deep learning methods. Despite their differences, only a small number of works belong to a unique class of methods. All the introduced methods are ranked in terms of effectiveness. In the end, we highlight the main ideas that have led the research on this topic. Regarding future work, we deemed that the most promising research directions are the domain flexibility and the end-to-end approaches.