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Emotion Detection Framework for Twitter Data Using Supervised Classifiers

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Data Engineering and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1079))

Abstract

“The task of emotion detection usually involves the analysis of text. Humans show universal consistency in identifying emotions however shows an excellent deal of variation between individuals in their abilities.” We have detected the emotion for Twitter messages as they provide rich ensemble of human emotions. We have used machine learning algorithms namely Naive Bayes (NB) and k-nearest neighbor algorithm (KNN) to detect the emotion of Twitter message and then classify the Twitter messages into four emotional categories. We also made a comparative study of two supervised machine learning algorithms; the eager learning classifier (NB) performed well when compared with lazy learning classifier (KNN).

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Author information

Authors and Affiliations

  1. SCETW, Hyderabad, India

    Matla Suhasini & Badugu Srinivasu

Authors
  1. Matla Suhasini
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  2. Badugu Srinivasu
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Corresponding author

Correspondence to Matla Suhasini .

Editor information

Editors and Affiliations

  1. Professor & Head, Department of Computer Science & Engineering, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  2. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Roman Senkerik

  3. Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    Satya Prasad Lanka

  4. Department of EEE, Stanley College of Engineering and Technology, Hyderabad, Telangana, India

    V. Rajagopal

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Suhasini, M., Srinivasu, B. (2020). Emotion Detection Framework for Twitter Data Using Supervised Classifiers. In: Raju, K.S., Senkerik, R., Lanka, S.P., Rajagopal, V. (eds) Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 1079. Springer, Singapore. https://doi.org/10.1007/978-981-15-1097-7_47

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