Sentiment Analysis as a Restricted NLP Problem

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Author(s): Akshi Kumar (Delhi Technological University, India)and Divya Gupta (Galgotias University, India)
Copyright: 2021
Pages: 32
Source title: Natural Language Processing for Global and Local Business
Source Author(s)/Editor(s): Fatih Pinarbasi (Istanbul Medipol University, Turkey)and M. Nurdan Taskiran (Istanbul Medipol University, Turkey)
DOI: 10.4018/978-1-7998-4240-8.ch004

Keywords: Business Information Systems / Business Science Reference / Knowledge Management and Library Science / Natural Language Processing

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Abstract

With the accelerated evolution of social networks, there is a tremendous increase in opinions by the people about products or services. While this user-generated content in natural language is intended to be valuable, its large amounts require use of content mining methods and NLP to uncover the knowledge for various tasks. In this study, sentiment analysis is used to analyze and understand the opinions of users using statistical approaches, knowledge-based approaches, hybrid approaches, and concept-based ontologies. Unfortunately, sentiment analysis also experiences a range of difficulties like colloquial words, negation handling, ambiguity in word sense, coreference resolution, which highlight another perspective emphasizing that sentiment analysis is certainly a restricted NLP problem. The purpose of this chapter is to discover how sentiment analysis is a restricted NLP problem. Thus, this chapter discussed the concept of sentiment analysis in the field of NLP and explored that sentiment analysis is a restricted NLP problem due to the sophisticated nature of natural language.

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