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Sentence-Level Emotion Detection Framework Using Rule-Based Classification

Content Provider	Springer Nature Link
Author	Asghar, Muhammad Zubair Khan, Aurangzeb Bibi, Afsana Kundi, Fazal Masud Ahmad, Hussain
Copyright Year	2017
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.
Starting Page	868
Ending Page	894
Page Count	27
File Format	PDF
ISSN	18669956
Journal	Cognitive Computation
Volume Number	9
Issue Number	6
e-ISSN	18669964
Language	English
Publisher	Springer US
Publisher Date	2017-09-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Opinion mining Sentiment analysis Emotion detection Emoticons Slang Neurosciences Computation by Abstract Devices Artificial Intelligence (incl. Robotics) Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Cognitive Neuroscience Computer Science Applications Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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