A Survey on Implicit Aspect Detection for Sentiment Analysis: Terminology, Issues, and Scope

Abstract

Sentiment analysis or opinion mining has come forth as an attractive research field in the past few years. Sentiment analysis extracts sentiments from the text for analysis and aggregation at different levels of detail. In aspect-level sentiment analysis, we aggregate sentiment for different aspects of entities. The bulk of the research work executed so far focuses on detecting explicit aspects but ignored implicit aspects, which are insinuated by other existing words and articulates of the sentence. Since a significant percentage of sentences contain implicit aspects, detection of implicit aspects becomes vital for sentiment analysis. This survey concentrates on implicit aspect detection, and a detailed discussion about state of the art is provided. The available methods are categorized depending on the algorithm applied. Quantitative evaluation for different methods as stated by authors is included for comparison purpose. Discussion about terminology, issues, and scope in the detection of implicit aspects is also included. The fine-grained sentiment information collected may be used in many applications in various domains. This survey aims to advocate the need for implicit aspect detection, determine existing efficient solutions, identify complications in implicit aspect detection, and suggest measures to improve performance, which comprise future research trends in implicit aspect detection.