Abstract
Sentiment analysis refers to the algorithmic extraction of subjective information from textual data and—driven by the increasing amount of online communication—has become one of the fastest growing research areas in computer science with applications in several domains. Although sports events such as football matches are accompanied by a huge public interest and large amount of related online communication, social media analysis in general and sentiment analysis in particular are almost unused tools in sports science so far. The present study tests the feasibility of lexicon-based tools of sentiment analysis with regard to football-related textual data on the microblogging platform Twitter. The sentiment of a total of 10,000 tweets with reference to ten top-level football matches was analyzed both manually by human annotators and algorithmically by means of publicly available sentiment analysis tools. Results show that the general sentiment of realistic sets (1000 tweets with a proportion of 60% having the same polarity) can be classified correctly with more than 95% accuracy. The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science.
Highlights
Science, to some extent, has always been an image of society, as a changing world imposes changing research areas and questions
The present paper demonstrates that sentiment analysis can be an effective and useful tool for sports-related content and is intended to stimulate the increased use of and discussion on sentiment analysis in sports science
Sports science has started to adapt to the era of Big Data, for example, by using positional data or tracking data in match analysis of various sports such as football [2], tennis [3], and basketball [4]
Summary
To some extent, has always been an image of society, as a changing world imposes changing research areas and questions. The invention and development of new sports, changes in demographics and recreational behavior or—as outlined in our approach—digitization and increasing volume of sports-related data are just some aspects influencing opportunities and relevance of research. Digitization has led to a massive increase of available data and data complexity in almost every aspect of life which is imposing new opportunities as well as new challenges and is often referred to as Big Data [1]. The increased technical and computational effort going hand in hand with these data-driven research areas has given rise to the discipline of computer science in sports
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