## 4 Results  
### Temporal Patterns  
The collected data is used to perform a temporal analysis on the behavior of message traffic for each company via Twitter to get descriptive temporal patterns associated with the days of the week and the hours when users transmit the most quantity of messages (Figure 1). Figure 1: Number of tweets per day of the week for each public utility company. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dictionary**} & \multicolumn{2}{c}{**Syzabet Library**} & \multicolumn{2}{c}{**Merge (Assignment)**} & \multirow{2}{*}{**Result**} \\ \cline{2-2} \cline{4-6}  & **No. Tweets Affected** & & & & \\ \hline Bing & 948 & 49.17\% & 1367 & 70.9\% & Assignment \\ AFINN & 728 & 37.75\% & 1226 & 63.59\% & Assignment \\ NRC & 845 & 43.82\% & 732 & 37.97\% & RStudio \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the evaluation of methods for sentiment analysis. In the case of the Acueducto, Thursdays and Fridays present the highest number of messages, with a marked trend for these days. On the other hand, Enel shows semi-homogeneous behavior on each of the days of the week. Finally, for Vanti, there is more traffic on Tuesdays and Wednesdays. It can be seen that day patterns are different within the three companies. Regarding the hours of the day (Figure 2), the three companies have similar behavior related to the traffic of tweets. The highest traffic starts at 10 am and finishes around midnight. The lowest traffic is related to nighttime periods from midnight until 9 am, when there is a change in tendency. Trends, seasonality, and temporal patterns were also studied by plotting the number of tweets as a time series. Results for Enel are shown in Figure 3.  
In the case of Enel (Figure 3), an average of 50 daily tweets was obtained during the period analyzed, with an upward trend at the end of the period. When verifying the messages in the outlier points of the series, it can be observed that between the months of April and May, the daily number increases compared to the average since there is a rainy season in Bogota that causes power outages. Therefore, the rainy season is highly correlated with massive failures in the continuity of service in several locations of the city and the surrounding municipalities. In addition, people communicate the consequences of this service failure by talking about food conservation, the operation of vital equipment for people, and the damage to household appliances and electronic equipment. Figure 3: Enel time series with average (blue) and trend (orange). Figure 2: Tweet traffic per hour of the day for each company. For the Acueducto (Figure 4), at the end of the period analyzed, there were atypical values due to high contactability explained by a service outage in the northwestern area of Bogota, where users reported no service for more than 72 h, which was labeled as an outlier situation. For the rest of the studied period, the Acueducto time series seems to have homogenous behavior. However, the time series was reanalyzed by normalizing it. This process consisted of taking the seven values closest to the left of the first peak and the six values following the second peak to normalize the series and make the result consistent with the other data. The new time series is presented in Figure 5.  
From this analysis, Acueducto received 57 daily tweets on average with an upward trend at the end of the period, and a seasonality of the data is identified by observing that on average, every 47 days, the company shows high traffic of messages exceeding 200 tweets. The peaks identified in the series report problems related to short service outages, water quality problems, and mobility problems due to the execution of public networks by Acueducto. In the case of Vanti, the results obtained in Figure 6 allow us to conclude that the time series shows an average of 18 daily tweets for the period analyzed, with a slight downward trend at the end of the period.