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A Learning Based Approach for Real-Time Emotion Classification of Tweets

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Applications of Social Media and Social Network Analysis

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Tmotion recognition can be done in a wide range of applications to enhance the user experience. Because of these many types of applications there are is a large range of different data types that can be processed, such as text, video, speech, sound, accelerometer data and various bio-sensor data types. In order to bring emotion recognition into everyday use, it is important to work with data types and sources that are available to everyone. Therefore in this chapter twitter data is used for emotion recognition. Since emotion recognition applications need to uncover the user’s emotion fast, the focus lies on real-time emotion classification. Sentiment analysis or emotion recognition research often uses a lexicon based approach, though in this chapter a learning based approach is used. Nine emotion classification algorithms are compared with focus on precision and timing. This chapter shows that accuracy can be enhanced by 5.83 % compared to the current state-of-the-art by improving the features and that the presented method work in real-time.

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Notes

  1. 1.

    www.instructables.com/id/social-networking-for-my-toaster/.

  2. 2.

    http://cnet.co/14LcGx9.

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Acknowledgments

This work was partly done within the Friendly Attac project (http://www.friendlyattac.be/), funded by the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT).

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Authors and Affiliations

  1. Department of Electronics and Information Systems, Ghent University, Gaston Crommenlaan 8, 9050, Ledeberg-Ghent, Belgium

    Olivier Janssens, Rik Van de Walle & Sofie Van Hoecke

Authors
  1. Olivier Janssens
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  2. Rik Van de Walle
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  3. Sofie Van Hoecke
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Corresponding author

Correspondence to Olivier Janssens .

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Editors and Affiliations

  1. Department of Computational Intelligence, Wroclaw University of Technology, Wroclaw, Poland

    Przemysław Kazienko

  2. Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, Indiana, USA

    Nitesh Chawla

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Janssens, O., de Walle, R.V., Hoecke, S.V. (2015). A Learning Based Approach for Real-Time Emotion Classification of Tweets. In: Kazienko, P., Chawla, N. (eds) Applications of Social Media and Social Network Analysis. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-19003-7_7

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  • DOI: https://doi.org/10.1007/978-3-319-19003-7_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19002-0

  • Online ISBN: 978-3-319-19003-7

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