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Social Big Data Mining: A Survey Focused on Sentiment Analysis

Social Big Data Mining: A Survey Focused on Sentiment Analysis

Anisha P. Rodrigues, Niranjan N. Chiplunkar, Roshan Fernandes
Copyright: © 2022 |Pages: 22
ISBN13: 9781668463031|ISBN10: 1668463032|EISBN13: 9781668463048
DOI: 10.4018/978-1-6684-6303-1.ch069
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MLA

Rodrigues, Anisha P., et al. "Social Big Data Mining: A Survey Focused on Sentiment Analysis." Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, IGI Global, 2022, pp. 1338-1359. https://doi.org/10.4018/978-1-6684-6303-1.ch069

APA

Rodrigues, A. P., Chiplunkar, N. N., & Fernandes, R. (2022). Social Big Data Mining: A Survey Focused on Sentiment Analysis. In I. Management Association (Ed.), Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines (pp. 1338-1359). IGI Global. https://doi.org/10.4018/978-1-6684-6303-1.ch069

Chicago

Rodrigues, Anisha P., Niranjan N. Chiplunkar, and Roshan Fernandes. "Social Big Data Mining: A Survey Focused on Sentiment Analysis." In Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines, edited by Information Resources Management Association, 1338-1359. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-6684-6303-1.ch069

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Abstract

Social media is used to share the data or information among the large group of people. Numerous forums, blogs, social networks, news reports, e-commerce websites, and many more online media play a role in sharing individual opinions. The data generated from these sources is huge and in unstructured format. Big data is a term used for data sets that are large or complex and that cannot be processed by traditional processing system. Sentimental analysis is one of the major data analytics applied on big data. It is a task of natural language processing to determine whether a text contains subjective information and what information it expresses. It helps in achieving various goals like the measurement of customer satisfaction, observing public mood on political movement, movie sales prediction, market intelligence, and many more. In this chapter, the authors present various techniques used for sentimental analysis and related work using these techniques. The chapter also presents open issues and challenges in sentimental analysis landscape.

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