Detecting real-time correlated simultaneous events in microblogs: The case of men’s Olympic football
Document Type
Conference Item
Publication Date
1-1-2021
Abstract
Although many predictive models have been designed to detect real-time events, there is still little progress in characterizing simultaneous events. Simultaneous events found in the sport domain can be used to understand how several correlated incidents occur at the same time to describe a specific phenomenon. We proposed a novel mechanism that uses Twitter messages in order to predict emotions associated with the final football match between Brazil and Germany in Rio Olympics 2016. Users’ opinions and their sentiments were extracted from the obtained tweets using the K-means clustering algorithm and the SentiStrength technique. We also applied the “Multi-label” classification technique in conjunction with the “Binary Relevance” (BR) method. The results showed that NaiveBayes was able to predict the match outcomes and related emotions with an accuracy value of 81 and a hamming loss value of 16. This study provides a robust approach to successfully detect real-time events using social media platforms. It also helps football clubs to characterize matches during the time span of the game. Finally, the proposed method contributes to the decision-making process in the sport domain. © 2021, Springer Nature Switzerland AG.
Keywords
Emotion, Football, Multi-label classification, Sentiment analysis, Twitter
Divisions
bisnesaccount
Funders
None
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume
12789
Event Title
3rd International Conference on HCI in Games, HCI-Games 2021, held as part of the 23rd International Conference, HCI International 2021
Event Location
Virtual, Online
Event Dates
24 - 29 July 2021
Event Type
conference