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Sentiment Analysis Techniques for Social Media Data: A Review

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First International Conference on Sustainable Technologies for Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1045))

Abstract

The world is going to digitize day by day. A lot of data generated by the social website users that play an essential role in decision-making . It is impossible to read the whole text, so sentiment analysis make it easy by providing the polarity to the text and classify text into positive and negative classes. Classification task can be performed by using different algorithms results in a different level of accuracy. The purpose of the survey is to provide an overview of various methods that deal with sentiment analysis. The review also presented a comparative analysis of various sentimental analysis techniques with their performance measurement.

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Author information

Authors and Affiliations

  1. Chandigarh University, Mohali, Punjab, India

    Dipti Sharma, Munish Sabharwal & Vinay Goyal

  2. Skyline University, Dubai, UAE

    Mohit Vij

Authors
  1. Dipti Sharma
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  2. Munish Sabharwal
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  3. Vinay Goyal
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  4. Mohit Vij
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Corresponding author

Correspondence to Dipti Sharma .

Editor information

Editors and Affiliations

  1. Department of Electrical and Communication Engineering, The Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Neumann János University, Kecskemét, Bács-Kiskun, Hungary

    Janos Arpad Kosa

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh Chandra Poonia

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Computer Science, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

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Sharma, D., Sabharwal, M., Goyal, V., Vij, M. (2020). Sentiment Analysis Techniques for Social Media Data: A Review. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_7

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