## 3 Results
### Qualitative Observations  
Algorithmic sentiment analysis is a challenging task and several general issues have been mentioned in the literature, such as sarcasm detection and order dependence [28], handling of spelling mistakes and slang [39] or intensification and negation [29].  
We will not repeat these known issues, but will demonstrate some qualitative observations and resulting challenges in sports-related Twitter data in line with the argument of [PERSON] and [PERSON] [28] (p. 13) on the issue of domain dependence in sentiment analysis: \"The same sentence or phrase can have different meanings in different domains. For example, the word 'unpredictable' is positive in the domain of movies, dramas, etc., but if the same word is used in the context of a vehicle's steering, then it has a negative opinion.\" Just as every other domain, sports in general and football in particular have their own vocabulary. We use the QDAP method to demonstrate some issues of football-related textual data being a consequence of not using a football-specific sentiment lexicon. Mentions of popular players such as [PERSON] are categorized as negative due to the meaning of his last name. The terms \"free kick\" and \"champions league\" are categorized as positive and the term \"penalty shootout\" as negative, although in football-related communication none of these terms expresses any sentiment by itself. At the same time, terms with a clear connotation such as the negative term \"diving\" or the positive term \"Mexican wave\" will be categorized as neutral. Sports-specific lexica would help to overcome these domain-specific problems, yet not the general limitations in sentiment analysis. So far, the applicability has been investigated in various domains including banks, cars, computers, cookware, hotels/travel destinations, movies, music and phones [29; 36], but we are not aware of any prior study focusing on the validation of manually annotated textual data from the sports domain.  
Another sports-specific issue is the length of tweets. The database contains a significant quantity of tweets consisting of only a few words, such as the following examples:  
Figure 1: Set accuracies for various numbers of tweets (n) and proportions of polarity (p) based on 10,000 repetitions.  
It may be presumed that in other domains, such as politics or finance, the average tweet length is higher than in the sports domain. To test this empirically, two comparative datasets were collected using a selection of politics-related hashtags (#brexit, #generalelection2019, #vote) and a selection of finance-related hashtags (#dowjones, #wallstreet, #stocks, #bitcoin). The average tweet length for cleaned tweets was 13.3 words for the football-related tweets, 20.8 words for politics-related tweets and 18.0 words for finance-related tweets. For the football-related data, a proportion of 49.3% of tweets contained ten or less words (politics: 28.1%, finance: 31.6%), and 21.6% of all cleaned tweets contained even five or less words (politics: 11.8%, finance: 8.8%). This is evidence that the tweet length can vary widely and--at least with regard to the investigated selection of hashtags--football-related tweets are rather short. The low number of words, and even lower number of evaluable positive or negative words, makes a correct classification difficult. The rather low accuracy of classifying single tweets thus might, to some extent, be an artefact of the low number of words in each tweet.