## 2 Methods
### Algorithmic Evaluation  
The algorithmic evaluation of tweets can be seen as a classification problem in which the tweets are assigned to two (positive vs. negative) or more categories. In the literature, the methods used are commonly divided into two groups: lexicon-based approaches and machine learning approaches [11; 28; 29]. Lexicon-based methods rely on a predefined list of words (lexicon) defining the semantic orientation of each word, such as positive and negative words or words with a specific positivity and negativity score. This can be considered rather simplifying as the sentiment of a piece of text is directly derived from the set of contained words by a rule-based approach. In machine learning approaches, representation of text can contain several features including single words or longer sequences of words (also referred to as n-grams) and the sentiment of a piece of text is derived by using this representation as an input for machine learning techniques such as Support Vector Machines. We focus on lexicon-based approaches within this study, as our primary aim is to validate the transferability and feasibility of sentiment analysis as a tool in the domain of sports science and currently, lexicon-based tools are still easier accessible.  
In its simplest form, lexicon-based approaches [28; 29] are based on the idea of having an extensive list of words that are either considered to have a positive or a negative meaning. Given the textual data of a tweet, the total number of words as well as the number of positive and negative words can be counted. By dividing the number of positive (negative) words by the number of total words, a positivity (negativity) score of the tweet is determined. The total sentiment of the tweet is calculated by subtracting positivity and negativity. By performing a median split, the tweets having different decimal sentiment scores can then be classified into the two categories of positive and negative.  
Three easily accessible tools of lexicon-based sentiment analysis are used within this study. The commercial LIWC 2015 software [30] (referred to as LIWC throughout the remaining paper) and the SentimentAnalysis package [31] available for the open source software R [32] and using the QDAP dictionary [33] (referred to as QDAP throughout the remaining paper), as well as a lexicon from the R _lexicon_ package [34] based on the SenticNet4 lexicon going back to the work of [PERSON] et al. [35] (referred to as SN throughout the remaining paper). Moreover, a combined evaluation considering the averaged results of all three tools is calculated and referred to as COMB throughout the remaining paper. Please note that only the capability of these tools in identifying positive and negative words and deriving the total sentiment of a tweet using a rule-based approach is tested. In particular, no conclusions should be drawn on the general usefulness of the LIWC software having more functions than the ones considered here or on the SenticNet4 approach possessing a far more complex methodological procedure than just the lexicon itself used here. The following example tweet (after preprocessing) is used to demonstrate how the lexicon-based and rule-based methods work.  
\"should have lost but won champion material livtot\"  
The algorithm underlying the QDAP_tool identifies all words categorized as positive in the lexicon (\"won\", \"champion\"), all words categorized as negative in the lexicon (\"lost\") and all wordsbeing in none of these categories (\"should,\" \"have,\" \"but,\" \"material,\" \"livtot\"). The tweet consists of eight words including two positive and one negative word, thus yielding a positivity of 2/8 = 0.25, a negativity of 1/8 = 0.125 and a total sentiment of 0.25 - 0.125 = 0.125.