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Sentence-Level Emotion Detection Framework Using Rule-Based Classification

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Abstract

Emotion detection and analysis aims at developing applications that can detect and analyse emotions expressed by the users in a given text. Such applications have received considerable attention from experts in computer science, psychology, communications and health care. Emotion-based sentiment analysis can be performed using supervised and unsupervised techniques. The existing studies using supervised and unsupervised emotion-based sentiment analysis are based on Ekman’s basic emotion model; have limited coverage of emotion-words, polarity shifters and negations; and lack emoticons and slang. The problems associated with existing approaches can be overcome by the development of an effective, sentence-level emotion-detection sentiment analysis system under a rule-based classification scheme with extended lexicon support and an enhanced model of emotion signals: emotion words, polarity shifters, negations, emoticons and slang. In this work, we propose a rule-based framework for emotion-based sentiment classification at the sentence level obtained from user reviews. The main contribution of this work is to integrate cognitive-based emotion theory (e.g. Ekman’s model) with sentiment analysis-based computational techniques (e.g. detection of emotion words, emoticons and slang) to detect and classify emotions from natural language text. The main focus is to improve the performance of state-of-the-art methods by including additional emotion-related signals, such as emotion words, emoticons, slang, polarity shifters and negations, to efficiently detect and classify emotions in user reviews. The improved results in terms of accuracy, precision, recall and F-measure demonstrate the superiority of the proposed method’s classification results compared with baseline methods. The framework is generalized and capable of classifying emotions in any domain.

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Notes

  1. https://www.paulekman.com/wp-content/uploads/2013/07/Basic-Emotions.pdf

  2. Emoticons, available at: http://netforbeginners.about.com/cs/netiquette101/a/bl_emoticons101.htm, last accessed on Nov 20, 2016.

  3. Emoticons, available at: http://www.sharpened.net/emoticons/, last accessed on Nov 20, 2016.

  4. http://www.nltk.org/_modules/nltk/stem/wordnet.html

  5. https://pypi.python.org/pypi/tweepy

  6. http://www.nltk.org/api/nltk.tokenize.html

  7. http://www.nltk.org/_modules/nltk/stem/wordnet.html

  8. https://pypi.python.org/pypi/aspell-python-py2/1.13

  9. https://github.com/bogdanneacsa/tts-master/tree/master/ISEAR

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Acknowledgements

We are grateful to Prof. Dr. Shakeel Ahmad, Institute of Computing, Gomal University, for facilitating us by providing a licenced software and manuals during the execution of this project. We also are thankful to Furqan Khan for the execution and maintenance of software needed for conducting the experiments in the Opinion Mining and Sentiment Analysis Lab.

Funding

The authors received no specific funding for this work.

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Authors and Affiliations

  1. Institute of Computing and Information Technology (ICIT), Gomal University, Dera Ismail Khan, Pakistan

    Muhammad Zubair Asghar, Afsana Bibi, Fazal Masud Kundi & Hussain Ahmad

  2. Department of Computer Science, University of Science and Technology, Bannu, Pakistan

    Aurangzeb Khan

Authors
  1. Muhammad Zubair Asghar
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Correspondence to Muhammad Zubair Asghar.

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The authors declare that they have no conflict of interest.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.

Human and Animal Rights

This study did not involve any experimental research on humans or animals; hence, an approval from an ethics committee was not applicable in this regard. The data collected from the online forums are publicly available data and no personally identifiable information of the forum users were collected or used for this study.

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Asghar, M.Z., Khan, A., Bibi, A. et al. Sentence-Level Emotion Detection Framework Using Rule-Based Classification. Cogn Comput 9, 868–894 (2017). https://doi.org/10.1007/s12559-017-9503-3

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  • DOI: https://doi.org/10.1007/s12559-017-9503-3

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