## 4 Discussion  
Based on the set accuracy, it can be said that the tools are accurate enough to correctly classify sets of a few hundreds or thousands of tweets. As an example, sets of 1000 tweets, where a proportion of 60% have the same polarity, are correctly classified in more than 95% of all cases. As a high number of tweets is by no means a problem in usual real-world applications, this result proves the validity of sentiment analysis methods in such applications. As argued in the Methods section, the accuracy of classifying sets of tweets does have a higher relevance in real-world applications than the classification of single tweets, and a high set accuracy is achieved, although the accuracy with regard to single tweets is rather limited. The accuracy of the sentiment analysis methods (63.6% for the best performing method and 67.4% when combining all methods) were significantly higher than 50%. This shows the general capability to identify sentiments correctly in single tweets as well, but still appears to be a rather weak result, given that the lower benchmark of 50% would be achieved by guessing. To make a fair assessment of this result, it needs to be discussed in light of the experimental set-up and the existing literature. As the algorithm is intended to have the highest possible agreement to human evaluation, an upper benchmark of 100% is unreachable, given the fact that even manual annotators will not reach full agreement due to the influence of subjectivity. As demonstrated in the Results section, the inter-rater-agreement in terms of accuracy was 94.5%, if considering only those tweets judged as either positive or negative by both annotators. Comparability to results stated in the literature is highly limited, as the accuracy does not only depend on the method, but also on the domain and the type of textual data [29]. Longer pieces of text, for example, imply an easier task for correct sentiment classification, while tweets are very short pieces of text and thus only comparison to literature reporting sentiment classification tasks of Twitter data seems reasonable. Accuracies around 75% have been reported for such tasks [26; 28], showing the general difficulty and relativizing the result found. Moreover, the aforementioned studies use machine learning techniques such as Support Vector Machines while the present study focused on techniques and tools that are purely lexicon-based and thus, easily available and feasible for non-computer scientists. The short length of football-related tweets, as described in the Results section, and further influence of domain dependence, as explained in the Methods section, could be other reasons for falling short of the accuracies reported in other domains. One area of future research could, therefore, be the improvement of methods and lexica for sports-related content. With regard to the different tools used, QDAP was able to slightly outperform LIWC and SN in terms of accuracy. To make a fair assessment of this result, it needs to be said that the SentimentAnalysis package in R and the QDAP lexicon are specifically provided for sentiment analysis as performed within this study. LIWC is a software for text analysis that has a broad scope of functions and only one aspect (positive and negative emotions) was used within the study. Moreover, the difference in accuracy seems to be partly attributable to the rather high number of tweets not containing any evaluable positive or evaluable negative word identifiable by LIWC. Regarding SN, we did not test the more complex SenticNet4 method as proposed by [PERSON] et al. [35], but just made use of a binary polarity lexicon (i.e., list of positive and negative words) deduced from their work. With reference to the set accuracy, it can be said that all tools perform comparably in real-world applications and that the number of tweets, as well as the polarity in the test data set, had a higher influence on the set accuracy than the choice of the tool. Sentiment analysis could have a significant impact in sports.