Vertaisarvioitu artikkeli kokoomateoksessa (A3)
Predicting Fans’ FIFA World Cup Team Preference from Tweets
Julkaisun tekijät: Md. Fazla Rabbi, Md. Saddam Hossain Mukta, Tanjima Nasreen Jenia, A. K. M. Najmul Islam
Toimittaja: Touhid Bhuiyan, Md Mostafijur Rahman, Md. Asraf Ali
Konferenssin vakiintunut nimi: International Conference on Cyber Security and Computer Science
Julkaisuvuosi: 2020
Journal: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Kirjan nimi *: Cyber Security and Computer Science. Second EAI International Conference, ICONCS 2020, Dhaka, Bangladesh, February 15-16, 2020, Proceedings
Sarjan nimi: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Volyymi: 325
Aloitussivu: 280
Lopetussivun numero: 292
ISBN: 978-3-030-52855-3
ISSN: 1867-8211
DOI: http://dx.doi.org/10.1007/978-3-030-52856-0_22
FIFA world cup is the most prestigious football tournament and widely viewed sporting event in the world. People support different teams (countries) of FIFA world cup based on players’ skills, number of winning trophies, and deliberate strategies that are applied by these teams during the tournament. These people share their opinion, criticism, love, and affection on the social media, i.e., Twitter. In this paper, we predict users’ FIFA world cup supporting preference from their tweets. First, we analyze user’s tweets and build two different types of classifiers by using LIWC and ELMo Word Embedding based techniques. These classifiers predict which team a user prefers from her word usage pattern in tweets. We find that Random Forest classifier performs the best for LIWC based model. We also find deep learning based word embedding technique, ELMo, achieves decent potential to predict users’ team supporting preference. Later, we build a multi-level weighted ensemble model to integrate both of the independent models, i.e., LIWC and ELMo. Our ensemble model shows substantial prediction potential (average accuracy-83.5%) to predict users’ FIFA world cup supporting preference from their tweets.