Detecting emotions in students’ generated content: An evaluation of EmoTect system

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4 Citations (Scopus)

Abstract

In this paper, an intelligent e-counselling system for automatic detection of emotion in text is evaluated. A support vector machine classifier was used for the development of the e-counselling system, hence we compared the performance of the e-counselling system’s classifier with WEKA’s Multinomial Naïve-Bayes and J48 decision tree classifiers. While this paper is geared towards ascertaining the efficacy of the various classifiers for classifying emotions in learners’ generated text content, this paper also aims to ascertain the performance of the e-counselling system for complementing decision making concerning students in counselling delivery. In building the system, an annotated students’ life story corpus was developed and used for the experiment. Therefore, 85% of the total instances of the life stories was used as training data while the remaining 15% was used as test data with sample instances of real-time data from students’ textual submission through the e-counselling system. The results of the experiment show that the SVM, implemented in our proposed e-counselling system, is superior over the MNB and J48 classifiers.

Original languageEnglish
Title of host publicationTechnology in Education
Subtitle of host publicationInnovative Solutions and Practices - 3rd International Conference, ICTE 2018, Revised Selected Papers
EditorsWai Shing Ho, Jeanne Lam, Will W. K. Ma, Simon K. Cheung, Kam Cheong Li, Oliver Au
PublisherSpringer Verlag
Pages235-248
Number of pages14
ISBN (Print)9789811300073
DOIs
Publication statusPublished - 2018
Externally publishedYes
Event3rd International Conference on Technology in Education, ICTE 2018 - Tseung Kwan O
Duration: 9 Jan 201811 Jan 2018

Publication series

NameCommunications in Computer and Information Science
Volume843
ISSN (Print)1865-0929

Conference

Conference3rd International Conference on Technology in Education, ICTE 2018
Country/TerritoryHong Kong
CityTseung Kwan O
Period9/01/1811/01/18

Keywords

  • Counselling
  • Decision making machine learning
  • Emotion detection
  • Students
  • Text classification

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