## 4 Experiments and Results
### Case Study  
The information available in TripAdvisor about 23 local restaurants was collected. As it may be seen in Figure 6, there is information about the number of reviews, the distance to the city centre, the range of prices, the types of food and the catering of special culinary requirements. There is also a rating for each domain attribute and a global rating of the restaurant. The ratings for the 23 alternatives are shown in Table 7. Interested readers may find the remaining details in TripAdvisor.  
Figure 5: Confusion matrices.  
We analysed the correlation between the TripAdvisor ratings and the results of the ABSA module as follows. First, we calculated the polarity index of each pair (alternative, aspect) given a set of reviews as described in Algorithm 2. Then, the overall polarity index of an alternative was obtained by taking the average of all the aspects. Finally, we converted the polarity index into a rating value from 0 to 5 by dividing the polarity index value by 20. The obtained correlations were 0.93 (food), 0.83 (service), 0.55 (price), 0.73 (ambience) and 0.87 (service). The correlation between the TripAdvisor ratings and the results of the ABSA module is shown in Figure 6.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Id** & **Restaurant Name** & **Overall** & **Food** & **Service** & **Price** & **Amb.** \\ \hline
1 & AQ & 4.5 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
2 & ELIAN Cafe Restaurant & 4.5 & 4.5 & 4.5 & 4.5 & 4.5 \\ \hline
3 & La Caleta & 4.5 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
4 & Tarakon & 4.5 & 4.5 & 4.5 & 4.5 & 4 \\ \hline
5 & Sadoll Restaurant & 4.5 & 4.5 & 4.5 & 4.5 & 4.5 \\ \hline
6 & El Taller & 4.5 & 4.5 & 4.5 & 4 & 4 \\ \hline
7 & El Trull & 4.5 & 4.5 & 4.5 & 4.5 & 4.5 \\ \hline
8 & Les Cques & 4 & 4 & 4 & 4 & 4 \\ \hline
9 & Mas Rosello & 4 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
10 & Les Fonts de Can Sala & 4.5 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
11 & La Xarca & 4.5 & 4.5 & 4.5 & 4.5 & 4 \\ \hline
12 & El Encuentro & 4.5 & 4 & 4 & 4.5 & 3.5 \\ \hline
13 & La Capital & 4 & 4 & 4.5 & 4 & 3.5 \\ \hline
14 & Barquet & 4.5 & 4.5 & 4 & 4 & 4 \\ \hline
15 & Arcs Restaurant & 4.5 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
16 & Restaurante Ca L Eulalia & 4.5 & 4.5 & 4 & 3.5 & 3.5 \\ \hline
17 & Restaurante Club Nautico Salou & 4.5 & 4.5 & 4.5 & 4 & 4.5 \\ \hline
18 & Octopussy & 3.5 & 3.5 & 3.5 & 3.5 & 3.5 \\ \hline
19 & Palermo 1962 S.c.p. & 4 & 4 & 4 & 4 & 3 \\ \hline
20 & The Cotton Club Restaurant \& Cocktails & 4.5 & 4.5 & 4.5 & 4.5 & 3.5 \\ \hline
21 & Lizarran Parc Central & 3 & 3.5 & 3.5 & 3 & 3 \\ \hline
22 & Buffalo Grill & 3 & 3.5 & 3.5 & 3 & 4 \\ \hline
23 & Indian Restaurant Mirchi Tarragona & 3 & 2 & 2 & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The alternatives and the ratings based on TripAdvisor.  
Figure 6: Restaurant details in TripAdvisor.  
0.8 (overall rating). Thus, the correlation between the overall rating provided by our system and the overall rating provided by TripAdvisor is strong. This shows the robustness of our sentiment analysis system and indicates that it is applicable to the restaurant domain. The low correlation in the \"price\" aspect may be attributed to the fact that people tend to give their opinion towards the quality of the service rather than the price, as we found that the \"price\" aspect has the lowest number of reviews. The \"ambience\" aspect shows an acceptable correlation with a value of 0.73; however, it is not quite strong. This can be attributed to the low performance of our system with respect to this aspect as shown in Table 4.  
As the results discussed above show the effectiveness of our sentiment analysis system, now we can use it to combine the preferences of the users with the analysis of the reviews to rank the restaurants. In the next subsection we will show the results of SentiRank considering two different kinds of users, and examining the influence of the addition of the social criteria derived from the sentiment analysis.