A Survey on Concept-Level Sentiment Analysis Techniques of Textual Data

Abstract

Text mining is one of the branches of data mining and refers to as the computing process of finding new patterns and relations among datasets which appear not to be related. Data mining is an interdisciplinary field which uses statistics, artificial intelligence, and database systems to generate new tools for discovering patterns among datasets. Similarly, when dealing with textual data, we need to use various methods in different branches of computer science (e.g. linguistics) and statistics. This study reviews the techniques of text-based sentiment analysis pipeline including preprocessing, aspect extraction, feature selection, and classification techniques used by scholars recently. It also surveys different applications of semantic analysis in the context of social media, marketing, and product reviews.