# Green Areas: The Secret of the Happiness of the People of Madrid
## 4 Results
### Sentiment Analysis  
The sentiment of the tweets was analyzed using the online analysis service provided by the MeaningCloud API [18], which is available on the RapidMiner platform [17]. This sentiment analysis classifies tweets into sixdifferent categories: very positive (P+), positive (P), neutral (NEU), negative (N), very negative (N+), and none (NONE). Table 3 shows samples of tweets annotated with a sentiment label. Our first analysis aimed to understand whether there were differences in sentiment expressed in the urban parks. Figure 3 shows the distribution of tweets labeled with sentiments per urban park, considering two categories: positive (P and P+) and negative (N and N+). The urban park with the most positive tweets was El Capricho, which had the highest percentage of positive tweets (45%) and only 5% negative ones. This difference in sentiment can be explained because it is a romantic historical park with restricted access and is open only on weekends. [PERSON] et al. [8] reported that sentiment is generally more positive during the weekend. Another interesting insight is that urban parks in the city's southern districts express a more positive sentiment. In particular, we refer to parks Cerro del Tio Pio and Emperatriz Maria de Austria, whose positive sentiment was 34% and 35%, respectively. However, the number of negative tweets was similar for all parks. The most negative tweets were found in the Dehesa de la Villa park. However, with 12% negative sentiment tweets, it cannot be concluded that users express more pessimistic views in this park, as it is still a relatively low percentage compared to that of positive ones, which was 26% in this case. Lastly, the last column of Figure 3 shows that the control dataset did not stand out because of its number of positive tweets, but it did with the negative ones. Indeed, it was the dataset with the most negative tweets, at 13%. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset** & **Num. Tweets** & **Dataset** & **Num. Tweets** \\ \hline Campo del Moro (CM) & 176 & Juan Carlos I (JCI) & 1792 \\ Capricho (C) & 1540 & Quinta de los Molinos (QM) & 573 \\ Casa de Campo (CC) & 1311 & Retiro (R) & 4050 \\ Cerro del Tio Pio (CTP) & 15 & Emp. Maria de Austria (EMA) & 81 \\ Dehesa de La Villa (DV) & 66 & & \\ \hline Total Urban parks (CM + C + CC + CTP + DV + JCI + QM + R + EMA) & 9604 \\ \hline National Park Guadarrama (G) & 11,244 & Control (Ctrl) & 54,112 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total amount of tweets per dataset after cleaning \begin{table}
\begin{tabular}{c c c} \hline \hline
**Tweet Message** & **Tweet Message (Translated)** & **Sentiment Label** \\ \hline Estupenda marána para pasear por el Parque del Retiro. & Wonderful morning to walk through the Retiro Park & P+ \\ \hline En el cumple de mi nieta. & At my granddaughter’s birthday. & P \\ \hline Hoy toca...por fin. La calor impide salir tanto... & Today it’s time... finally. The heat prevents you from & NEU \\  & going out so much... & \\ \hline Ya echo de menos Madrid. & I already miss Madrid. & N \\ \hline Podria estar horas limpiando el parque, de mierda, & I could spend hours cleaning the shit of the park, & N+ \\  & mierda de gente irresponsable. & shitty irresponsible people. & NONE \\ \hline Mi bosque particular. & My private forest. & NONE \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of tweets annotated with a sentiment label. The second analysis that we carried out aimed to determine if there were differences in the sentiment expressed in urban and national parks. In the Madrid region, there is only one national park, Guadarrama. Figure 4 shows the sentiment distribution for urban parks, national parks, and the control dataset, and there was a significant difference. While Guadarrama National Park had the same distribution of positive and negative tweets, urban parks had three times more positive tweets.