Soccer Fans Sentiment through the Eye of Big Data: The UEFA Champions League as a Case Study

The proliferation of online soccer contents generated by fans and teams has been widely leveraged in the area of sentiment analysis research. This work proposes domain-specific approach for understanding sentiment expressed in soccer conversations. We introduce a soccer-specific lexicon that we leverage in building sentiment model trained on soccer dataset. Our results show the effectiveness of the proposed approach in recognizing fans emotion during soccer events.

I. Introduction

Soccer is, by far, the most popular sport in the world among 8000 different types. With 3.5 billion fans ¹

http://www.topendsports.com/world/lists/popular-sport/fans.htm

and approximately 265 million players ²

https://www.fifa.com/mm/document/fifafacts/bcoffsurv/

around the world, soccer's success in gathering the attention of the public and the media is notable. In 2014, Twitter reported 672 million tweets related to the FIFA World Cup which took place in that year ³

https://www.fifa.com/mm/document/fifafacts/bcoffsurv/

. A block of 18,027,966 tweets relating to the English Premier League Soccer, were collected from Twitter in 2016 [1]. This proves that Twitter has become a venue for soccer fans to discuss everything about their favorite teams and players' performances throughout the season.