Vertaisarvioitu artikkeli kokoomateoksessa (A3)

Predicting Fans’ FIFA World Cup Team Preference from Tweets




Julkaisun tekijätMd. Fazla Rabbi, Md. Saddam Hossain Mukta, Tanjima Nasreen Jenia, A. K. M. Najmul Islam

ToimittajaTouhid Bhuiyan, Md Mostafijur Rahman, Md. Asraf Ali

Konferenssin vakiintunut nimiInternational Conference on Cyber Security and Computer Science

Julkaisuvuosi2020

JournalLecture 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 nimiLecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Volyymi325

Aloitussivu280

Lopetussivun numero292

ISBN978-3-030-52855-3

ISSN1867-8211

DOIhttp://dx.doi.org/10.1007/978-3-030-52856-0_22


Tiivistelmä

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.


Last updated on 2021-24-06 at 08:38