WEB MINING IN CLASSIFYING YOUTH EMOTIONS
DOI:
https://doi.org/10.24191/mjoc.v3i1.4748Keywords:
Emotion analysis, KPop, Social Media, Twitter Mining, Web MiningAbstract
Social media sites are websites used as mediums to create and share various types of contents over the internet. These sites can also be accessed through applications on mobile gadgets. Different social media sites are available for free, and most teenagers or youths have at least one active account. They use social media sites to connect and share their online profiles, daily activities, stories, and emotions. Depending on their social settings, their activities may or may not be seen by others. One of the latest trends that is spreading over the social media is the Korean Pop entertainment or popularly known as KPop. Over the social media, youths share and express how they feel about their Korean celebrities, music, and drama. However, the issue of excessive sharing of emotion-sharing over social media may increase the risk of mental illness and affect their mental health. Their obsession to keep up-to-date with their idols might lead or cause adverse consequences on their emotional states of mind. Thus, the aim of this research is to study the changes of youths’ emotions in two different countries which are Malaysia and Korea that are related to the KPop trend. We extract texts from tweets from Twitter social media sites using the Twitter API as the basis of our study. Then, the keyword 'KPop' is used to filter the tweets. Web mining model classifies the 12,000 tweets into six emotion categories, which are joy, sadness, fear, anger, disgust, and surprise. The system then records the emotion changes and the triggering events respectively.
References
Birjali, M., Beni-Hssane, A., & Erritali, M. (2017). Machine Learning and Semantic Sentiment Analysis based Algorithms for Suicide Sentiment Prediction in Social Networks. Procedia Computer Science, 113, 65-72.
Jurafsky, D., & Martin, J. H (2017). Speech and Language Processing. All rights reserved. Draft of August 7, 2017. Copyright c 2016. All rights reserved.
Nandhini, B. S., & Sheeba, J. (2015). Online social network bullying detection using intelligence techniques. Procedia Computer Science, 45, 485-492.
Narayanan, V., Arora, I., & Bhatia, A. (2013). Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model. In H. Yin, K. Tang, Y. Gao, F. Klawonn, M. Lee,
Neethu, M., & Rajasree, R. (2013). Sentiment analysis in twitter using machine learning techniques. Paper presented at the Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), 2013.
Pantic, I. (2014). Online Social Networking and Mental Health. Cyberpsychology, Behavior and Social Networking, 17(10), 652–657. http://doi.org/10.1089/cyber.2014.0070
Qamar, S., & Ahmad, P. (2015). Emotion Detection from Text using Fuzzy Logic. International Journal of Computer Applications, 121(3). Pg no
Seo, M., S. (2012). Lessons from K-pop’s Global Success, 5(3), 60. Retrieved from http://connection.ebscohost.com/c/articles/77971026/lessons-from-k-pops-global-success
Weise, T., Li, B., & Yao, X. (Eds.) (2013). Intelligent Data Engineering and Automated Learning – IDEAL 2013: 14th International Conference, IDEAL 2013, Hefei, China, October 20-23, 2013. Proceedings (pp. 194-201). Berlin, Heidelberg: Springer Berlin Heidelberg.
Yoo, S., Song, J., & Jeong, O. (2018). Social media contents-based sentiment analysis and prediction system. Expert Systems with Applications, 105, 102-111.
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Copyright (c) 2018 Zura Izlita Razak, Shuzlina Abdul-Rahman, Sofianita Mutalib, Nurzeatul Hamimah Abdul Hamid

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