Integrating Semantic Acquaintance for Sentiment Analysis

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Author(s): Neha Gupta (Manav Rachna International Institute of Research and Studies, Faridabad, India)and Rashmi Agrawal (Manav Rachna International Institute of Research and Studies, Faridabad, India)
Copyright: 2021
Pages: 20
Source title: Advanced Concepts, Methods, and Applications in Semantic Computing
Source Author(s)/Editor(s): Olawande Daramola (Cape Peninsula University of Technology, South Africa)and Thomas Moser (St. Pölten University of Applied Sciences, Austria)
DOI: 10.4018/978-1-7998-6697-8.ch005

Keywords: Computational Linguistics / Computer Science & IT / Data Analysis and Statistics / Information Science Reference

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Abstract

The use of emerging digital information has become significant and exponential, as well as the boom of social media (forms, blogs, and social networks). Sentiment analysis concerns the statistical analysis of the views expressed in written texts. In appropriate evaluations of the emotional context, semantics plays an important role. The analysis is generally done from two viewpoints: how semantics are coded in sentimental instruments, such as lexicon, corporate, and ontological, and how automated systems determine feelings on social data. Two approaches to evaluate sentiments are commonly adopted (i.e., approaches focused on machine learning algorithms and semantic approaches). The precise testing in this area was increased by the already advanced semantic technology. This chapter focuses on semantic guidance-based sentiment analysis approaches. The Twitter/Facebook data will provide a semantically enhanced technique for annotation of sentiment polarity.

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