Abstract
Opinion mining or sentiment analysis is the computational study of people’s opinions, appraisals, attitudes, and emotions toward entities such as products, services, organizations, individuals, events, and their different aspects. It has been an active research area in natural language processing and Web mining in recent years. Researchers have studied opinion mining at the document, sentence and aspect levels. Aspect-level (called aspect-based opinion mining) is often desired in practical applications as it provides the detailed opinions or sentiments about different aspects of entities and entities themselves, which are usually required for action. Aspect extraction and entity extraction are thus two core tasks of aspect-based opinion mining. In this chapter, we provide a broad overview of the tasks and the current state-of-the-art extraction techniques.
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Zhang, L., Liu, B. (2014). Aspect and Entity Extraction for Opinion Mining. In: Chu, W. (eds) Data Mining and Knowledge Discovery for Big Data. Studies in Big Data, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40837-3_1
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