## 2 Methods
### Preprocessing of Data  
Tweets contain a lot of information that cannot be usefully processed by lexicon-based sentiment analysis tools. Therefore, the following steps to clean the data--being in line with common approaches in the literature [27, 28]--were executed automatically prior to the algorithmic evaluation:  
- Removing all URLs (\"[[https://t.co/hSizQPxVFy](https://t.co/hSizQPxVFy)]([https://t.co/hSizQPxVFy](https://t.co/hSizQPxVFy))\") and mentions (\"@CardiffCityFC\")  
- Removing emoticons not analyzable as provided by the Twitter API in R (\"<U+009F>\")  
- Removing punctuation characters and digits  
- Removing tabs, line breaks and unnecessary spaces  
- Removing any non-ascii character  
- Changing text to lower case  
- Replacing contractions with full forms (\"wouldn't\" \(\rightarrow\) \"would not\")  
- Replacing acronyms with full forms (\"omg\" \(\rightarrow\) \"oh my god\")  
- Correcting and doubling intentionally misspelled words that express some sort of intensification (\"niiiiiiiiiiiiiiiiiiiice\" \(\rightarrow\) \"nice nice\")  
Although it is not uncommon to completely remove hashtags, we kept them included to consider those hashtags that express a sentiment such as #boring (i.e., removing the #-sign, but not the word) at the cost of also including specific hashtags that will not be included in any lexicon such as #livtot. Intentionally misspelled words repeating the same character various times are interpreted as a sign of intensification. As lexicon-based methods are used, the word will not be identified if kept in the given state. Therefore, the word is corrected in order to be identifiable in the lexicon, but doubled to account for the underlying intensification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Date** & **Match** & **Competition** & **Hashtag** & **Number of Tweets** \\ \hline
20 February 2019 & Atletic Madrid vs. Juventus Turin & Champions League & \#AltefulJuvve & 5121 \\
24 February 2019 & Manchester United vs. Liverpool & Premier League & \#MUNILV & 30,044 \\
7 March 2019 & Chelsea vs. Dynamo Kiev & Europa League & \#CHEDYN & 3262 \\
31 March 2019 & Cardiff City vs. Chelsea & Premier League & \#CARCHE & 13,114 \\
31 March 2019 & FC Liverpool vs. Tottenham Hotspur & Premier League & \#LIVTOT & 17,503 \\
10 April 2019 & Ajax Amsterdam vs. Juventus Turin & Champions League & \#AJAJUV & 2213 \\
16 April 2019 & FC Barcelona vs. Manchester United & Champions League & \#CRBMUN & 3615 \\
1 May 2019 & FC Barcelona vs. FC Liverpool & Champions League & \#FCBILV & 3603 \\
25 May 2019 & RB Leipzig vs. Bayern Munich & German Cup & \#RBLFCB & 1252 \\
29 May 2019 & Chelsea vs. Arsenal & Europa League & \#UELfinal & 5096 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary information on the dataset.  
As a result of preprocessing, an original uncleaned tweet  
\"It's just 38\" mins into the match and @ChelseaFcare@ChelseaFC like this I'm sorry to say this Chelsea are really loosing their standards as a big team [PERSON] lick <f0><U+009F><U+0091><U+0085>my ass like I care y'all trash <f0><U+009F><U+0097><U+0091> #CARCHE\"  
will be changed to this cleaned tweet after preprocessing  
\"it is just mins into the match and like this i am sorry to say this chelsea are really loosing their standards as a big team [PERSON] lick my ass like i care you all trash carche\".