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Text-based emotion prediction system using machine learning approach

Ahmad Fakhri, Ab. Nasir and Eng, Seok Nee and Chun, Sern Choong and Ahmad Shahrizan, Abdul Ghani and Anwar, P. P. Abdul Majeed and Asrul, Adam and Mhd, Furqan (2020) Text-based emotion prediction system using machine learning approach. In: 6th International Conference on Software Engineering and Computer Systems, ICSECS 2019, 25 - 27 September 2019 , Vistana Kuantan City Center, Kuantan, Pahang. pp. 1-11., 769 (1). ISSN 1757-8981

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Abstract

Text-based input becomes a common channel for humans in sharing their opinions/emotions to the product or service through online social media, shopping platform etc. Humans are easy to make errors in interpreting emotions, especially the emotion that derived from text based. The main aim of this study is to develop text-based emotion recognition and prediction system. Several market challenges facing in the advancement of emotion analysis with accuracy being the main issue. Therefore, four supervised machine learning classification algorithms such as Multinomial Naïve Bayes, Support Vector Machine, Decision Trees, and kNearest Neighbors were investigated. The model was developed based on Ekman’s six basic emotions which are anger, fear, disgust, joy, guilt and sadness. Data pre-processing techniques such as stemming, stop-words, digits and punctuation marks removal, spelling correction, and tokenization were implemented. A benchmark of ISEAR (International Survey on Emotion Antecedents and Reactions) dataset was used to test all models. Multinomial Naïve Bayes classifier resulted the best performance with an average accuracy of 64.08%. Finally, the best model was integrated to graphical user interface using Python Tkinter library to complete the whole system development. Besides, the detailed performance of the best model such as tf-idf and count vectorizer, confusion matrix, precision-recall rate, as well as ROC (Receiver Operating Characteristic) score were also discussed. Text-based emotion prediction system to interpret and understand human emotions was successfully developed.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Multinomial naïve bayes; Support vector machine; Decision trees; Receiver operating characteristic (ROC)
Subjects: Q Science > QA Mathematics > QA76 Computer software
Faculty/Division: Faculty of Manufacturing and Mechatronic Engineering Technology
College of Engineering
Depositing User: Pn. Hazlinda Abd Rahman
Date Deposited: 17 Dec 2020 03:35
Last Modified: 17 Dec 2020 03:35
URI: http://umpir.ump.edu.my/id/eprint/27733
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