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Sentiment Analysis and Mining of Opinions

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Internet of Things and Big Data Analytics Toward Next-Generation Intelligence

Part of the book series: Studies in Big Data ((SBD,volume 30))

Abstract

Now days the way of expressing opinions on certain products that people purchase and the services that they receive in the various industries has been transformed considerably because of World Wide Web. Social Networking sites fascinate people to post feedbacks and reviews online on blogs, Internet forums, review portals and much more. These opinions play a very important role for customers and product manufacturers as they tend to give better knowledge of buying and selling by setting positive and negative comments on products and other information which can improve their decision making policies. Mining of such opinions have focused the researchers to pay a keen intention in developing such a system which can not only collect useful and relevant reviews online in a ranked manner and also produce an effective summary of such reviews collected on different products according to their respective domains. However, there is little evidence that researchers have approached this issue in opinion mining with the intent of developing such a system. Our work will focus on what opinion mining is the existing works on opinion mining, the challenges in the existing techniques and the workflow of mining opinions. Consequently, the aim of this chapter is to discuss the overall novel architecture of developing an opinion system that will address the remaining challenges and provide an overview of how to mine opinions. Existing research in sentiment analysis tend to focus on finding out how to classify the opinions and produce a collaborative summary in their respective domains, despite an increase in the field of opinion mining and its research, many challenges remain in designing a more comprehensive way of building a system to mine opinions. This chapter addresses the problem of how to classify sentiments and develop the opinion system by combining theories of supervised learning.

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Author information

Authors and Affiliations

  1. CS department, Banasthali University, Vanasthali, India

    Surbhi Bhatia & Manisha Sharma

  2. CSE Department, YMCA University of Science and Technology, Faridabad, India

    Komal Kumar Bhatia

Authors
  1. Surbhi Bhatia
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  2. Manisha Sharma
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  3. Komal Kumar Bhatia
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Corresponding author

Correspondence to Surbhi Bhatia .

Editor information

Editors and Affiliations

  1. Techno India College of Technology , Kolkata, West Bengal, India

    Nilanjan Dey

  2. Cairo University , Cairo, Egypt

    Aboul Ella Hassanien

  3. Charotar University of Science and Tech , Changa, Gujarat, India

    Chintan Bhatt

  4. Tanta University , Tanta, Egypt

    Amira S. Ashour

  5. Dept of Computer Science and Engg, PVP Siddhartha Institute of Technology Dept of Computer Science and Engg, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

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Bhatia, S., Sharma, M., Bhatia, K.K. (2018). Sentiment Analysis and Mining of Opinions. In: Dey, N., Hassanien, A., Bhatt, C., Ashour, A., Satapathy, S. (eds) Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Studies in Big Data, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-319-60435-0_20

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  • DOI: https://doi.org/10.1007/978-3-319-60435-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60434-3

  • Online ISBN: 978-3-319-60435-0

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