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2016 | OriginalPaper | Chapter

Fundamentals of Sentiment Analysis: Concepts and Methodology

Authors : A. B. Pawar, M. A. Jawale, D. N. Kyatanavar

Published in: Sentiment Analysis and Ontology Engineering

Publisher: Springer International Publishing

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Abstract

Internet has opened the new doors for information exchange and the growth of social media has created unprecedented opportunities for citizens to publicly raise their opinions, but it has serious bottlenecks when it comes to do analysis of these opinions. Even urgency to gain a real time understanding of citizens concerns has grown very rapidly. Since, the viral nature of social media which is fast and distributed one, some issues get rapidly distributed and unpredictably become important through this word of mouth opinions expressed online which in turn has known as sentiments of the users. The decision makers and people do not yet realized to make sense of this mass communication and interact sensibly with thousands of others with the help of sentiment analysis. To understand thoroughly use of sentiment analysis in today’s business world, this chapter covers the brief about sentiment analysis including introduction of sentiment analysis, early history of sentiment analysis, problems of sentiment analysis, basic concepts of sentiment analysis with mathematical treatment, sentiment and subjectivity classification comprises of opinion mining and summarization, past scenarios of opinion or sentiment collection and their analysis. Methodologies like Sentiment Analysis as Text Classification Problem, Sentiment analysis as Feature Classification with mathematical treatment are explored. Also, Economic consequences of sentiment analysis on individual, society and organization with the help of social media sentiment analysis are provided as supporting component.

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Literature
1.
go back to reference Dalal, M.K., Zave, M.A.: Automatic text classification: a technical review. Int. J. Comput. Appl. (0975–8887) 28(2), 37–40 (2011) Dalal, M.K., Zave, M.A.: Automatic text classification: a technical review. Int. J. Comput. Appl. (0975–8887) 28(2), 37–40 (2011)
2.
go back to reference Eirinaki, M., Pisal, S., Singh, J.: Feature-based opinion mining and ranking. J. Comput. Syst. Sci. 1175–1184 (2012) Eirinaki, M., Pisal, S., Singh, J.: Feature-based opinion mining and ranking. J. Comput. Syst. Sci. 1175–1184 (2012)
6.
7.
go back to reference Jawale, M.A., Dr., Kyatanavar, D.N., Pawar, A.B.: Development of automated sentiment or opinion discovery system: review. In: Proceedings of ICRTET 2013 (2013) Jawale, M.A., Dr., Kyatanavar, D.N., Pawar, A.B.: Development of automated sentiment or opinion discovery system: review. In: Proceedings of ICRTET 2013 (2013)
8.
go back to reference Jawale, M.A., Dr., Kyatanavar, D.N., Pawar, A.B.: Implementation of automated sentiment discovery system. In: Proceedings of IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE- 2014), pp. 1–6 (2014). ISBN: 978-1-4799-4041-7 Jawale, M.A., Dr., Kyatanavar, D.N., Pawar, A.B.: Implementation of automated sentiment discovery system. In: Proceedings of IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE- 2014), pp. 1–6 (2014). ISBN: 978-1-4799-4041-7
9.
go back to reference Leong, C.K., Lee, Y.H., Mak, W.K.: Mining sentiments in SMS texts for teaching evaluation. In: Expert Systems with Applications, pp. 2584–2589 (2012) Leong, C.K., Lee, Y.H., Mak, W.K.: Mining sentiments in SMS texts for teaching evaluation. In: Expert Systems with Applications, pp. 2584–2589 (2012)
10.
go back to reference Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, 2nd edn. pp. 1–38 (2012) Liu, B.: Sentiment analysis and subjectivity. In: Handbook of Natural Language Processing, 2nd edn. pp. 1–38 (2012)
11.
go back to reference Liu, B.: Sentiment analysis: a multi-faceted problem. In: IEEE Intelligent Systems, pp. 1–5 (2010) Liu, B.: Sentiment analysis: a multi-faceted problem. In: IEEE Intelligent Systems, pp. 1–5 (2010)
12.
go back to reference Rainie, L., Horrigan, J.: Election 2006 online, Pew Internet & American Life Project Report, Jan 2007 Rainie, L., Horrigan, J.: Election 2006 online, Pew Internet & American Life Project Report, Jan 2007
13.
go back to reference Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. In: Science Direct, Expert Systems with Applications, pp. 10760–10773 (2009) Tang, H., Tan, S., Cheng, X.: A survey on sentiment detection of reviews. In: Science Direct, Expert Systems with Applications, pp. 10760–10773 (2009)
14.
go back to reference Yin, C., Peng, Q.: Sentiment analysis for product features in Chinese reviews based on semantic association. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 82–85 (2009) Yin, C., Peng, Q.: Sentiment analysis for product features in Chinese reviews based on semantic association. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 82–85 (2009)
Metadata
Title
Fundamentals of Sentiment Analysis: Concepts and Methodology
Authors
A. B. Pawar
M. A. Jawale
D. N. Kyatanavar
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-30319-2_2

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