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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: © 2020 |Pages: 22
ISBN13: 9781522596431|ISBN10: 1522596437|ISBN13 Softcover: 9781522596448|EISBN13: 9781522596455
DOI: 10.4018/978-1-5225-9643-1.ch025
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MLA

Rodrigues, Anisha P., et al. "Social Big Data Mining: A Survey Focused on Sentiment Analysis." Handbook of Research on Emerging Trends and Applications of Machine Learning, edited by Arun Solanki, et al., IGI Global, 2020, pp. 528-549. https://doi.org/10.4018/978-1-5225-9643-1.ch025

APA

Rodrigues, A. P., Chiplunkar, N. N., & Fernandes, R. (2020). Social Big Data Mining: A Survey Focused on Sentiment Analysis. In A. Solanki, S. Kumar, & A. Nayyar (Eds.), Handbook of Research on Emerging Trends and Applications of Machine Learning (pp. 528-549). IGI Global. https://doi.org/10.4018/978-1-5225-9643-1.ch025

Chicago

Rodrigues, Anisha P., Niranjan N. Chiplunkar, and Roshan Fernandes. "Social Big Data Mining: A Survey Focused on Sentiment Analysis." In Handbook of Research on Emerging Trends and Applications of Machine Learning, edited by Arun Solanki, Sandeep Kumar, and Anand Nayyar, 528-549. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9643-1.ch025

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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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