Implicit Aspect Extraction Techniques in Sentiment Analysis: A Survey

Abstract

Sentiment analysis is a Natural Language Processing (NLP) research field that uses texture data and machine learning approaches in analyzing sentiments, behaviors, and emotions. People use social media to express feelings in various forms of sentiment. Feelings of fear, worry, sadness, anger, and gratitude were expressed in an online social network. It might sometimes be difficult to get the proper sentiment associated to the aspect. Several feedback texts in which the sentiment is expressed indirectly or implicitly. The task of detecting and extracting terms important for opinion mining and sentiment analysis, such as terms for product qualities or features, is known as aspect extraction. The primary purpose of this research was to discuss and classify techniques for implicit aspect extraction or feature extraction, as well as to address previous research works on sentiment analysis. A few limitations and challenges had been discovered from the previous studies and the future direction of sentiment analysis can be explored further in more depth.