Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

The Role of Team Identification and Sentiment Analysis of Iranian Football Clubs’ Fans in Twitter Social Media with Natural Language Processing

Document Type : Excerpt from doctoral thesis

Authors
Leila Heidari 1
Najaf Aghaei 2
Hossein Akbari Yazdi 2
1 Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran
2 Department of Sport Management, Faculty of Physical Education and Sports Sciences, Kharazmi University, Tehran, Iran
JABM.3.2.15564.35887879085849695
Abstract
Introduction: Natural Language Processing (NLP) is a vital subfield of artificial intelligence that helps computers understand and interpret human language. The purpose of this research was to identify and understand the sentiments expressed by football fans on Twitter, especially after special events such as the Premier League, and to present a model based on their team identification level.

Methods: The current research is applied research in terms of its purpose and descriptive-analytical research in terms of its nature. The statistical population included all the tweets generated on Twitter by the fans of the clubs present in the 2021/22 Iranian Premier Football League. Therefore, 5560 tweets were collected after 120 games of the second-half season of the Premier Football League. Special Lexicon and sentiment classifiers based on artificial intelligence (VADER dictionary) were used in a Python environment as a sentiment analysis tool. A Regression model was used to predict fans'' sentiments.

Results: The results of natural language processing showed that positive sentiments were the most expressed emotions by fans. The regression model predicted fans'' sentiments based on factors such as the level of team identification and the result of winning and losing the game, and the conceptual model of the research was confirmed.

Conclusion: This study can be used by media and marketing managers of teams who seek a deeper understanding of the emotional dynamics of football fans.
Keywords
artificial intelligence
data mining
premier league
user-generated content
Subjects
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