## 1 Introduction
We conducted a search for articles related to social media analysis by means of sentiment analysis for two well-known multidisciplinary sports journals: The _Journal of Sports Sciences_ and the _European Journal of Sport Science_. For both journals, not a single article title included the terms \"sentiment analysis\" or \"opinion mining.\" Even for the term \"social media,\" only one survey on the use of social media by sports nutritionists in the _Journal of Sports Sciences_[15] and an editorial explaining the increased use of social media by the _European Journal of Sports Sciences_[16] were found. A search for the terms \"sentiment analysis\" or \"opinion mining\" in the Web of Science Core Collection resulted in 1114 articles, but not a single article resulted when filtering for the Web of Science Category \"sport sciences.\" Nevertheless, some literature linking social media analysis and sports exists, such as investigations on the use of Twitter by sports journalists, athletes or fans [17; 18; 19], but are not related to algorithmic sentiment analysis. Moreover, a few applications of sentiment analysis in sports forecasting exist [20; 21; 22], however, these studies are focused on applying sentiment analysis in predictive tasks instead of validating sentiment analysis methods. In light of the existing literature, algorithmic analysis of social media content can be considered a widely unused approach in sports science so far, although numerous possible applications exist. Table 1 shows a list of disciplines in sports science that could profit from its potential for several research questions. Driven by the large body of literature on sentiment analysis in general, but the absence of articles in sports science, the present study validates the appropriateness of sentiment analysis approaches in a sports-related context focusing on tweets originating from the microblogging platform Twitter. Despite all the opportunities, caution and preliminary work is required as most methods of sentiment analysis have been developed for, validated in and used in other domains such as economics or politics, and taking over a tool from other domains without proper validation seems unreasonable. The discrepancy between extensive research on sentiment analysis methods and rare, if any, research applying it to sports science is evident and it may be reasoned that sports scientists are either not aware of the possibilities of sentiment analysis, not convinced by its accuracy, or deterred by its complexity. In this sense, there exists a trade-off between a high accuracy on the one side and a high practicability on the other side, as sentiment analysis methods would be used as a tool in sports science and not as the main subject of investigation. Techniques applicable in sports science consequently need an acceptable accuracy, however, even the most accurate technique is useless if not accessible or not possessing a reasonable degree of complexity. The goal is, thus, not to find or develop a gold-standard sentiment analysis technique in sports, but to validate the current applicability of easily accessible sentiment analysis tools. Therefore, we chose to limit our study to lexicon-based methods of sentiment analysis, analyzing and comparing three relatively easily accessible tools. Machine learning approaches or the development of improved methods of sentiment analysis are not within the scope of this study. The contributions of this article are the following: 10,000 tweets written by users on Twitter and related to 10 top-class football matches were evaluated both manually and algorithmically in order to validate the accuracy of algorithmic sentiment analysis in football-related textual data. To the best of our knowledge, no study so far has focused on the validation of sentiment analysis tools based on manually annotated tweets from the sports domain. Moreover, an accuracy measure designed for real-world applications is introduced and evaluates the accuracy with regard to realistic sets of tweets instead of single tweets.