# Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies
## 4 Experimental Results
We checked the comments related to these features to see what they meant: (a) high speed--customers like the discount for the high speed rail ticket booking available on the Klook platform; (b) food court--customers mentioned that it was easy to reach the food court from the tourist attractions; (c) front desk--the customers' experience of the Klook service was better than that of front desk service; (d) hot spring--customers mentioned that Klook provided a satisfactory discount on the activity fee; (e) mrt station--customers mentioned that the pick-up points were near the MRT station; and (f) rock formation--customers were satisfied with the shuttle bus tickets to Yehliu Geopark. KKday should be aware of these threats coming from its competitor, and it could utilize its own strengths to lessen these threats. 4. Opportunities  
Features with below-average FPS scores, representing negative comments about Klook, are regarded as opportunities to KKday. Among the comments collected up to now, no features met these conditions. One of the possible reasons for this is that the number of reviews collected was insufficient. Another possible reason is that most customers are satisfied with Klook. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Reference**} & **Competitor** \\ \cline{3-4}  & & **KKday** & **Klook** \\ \hline \multirow{2}{*}{**tour guide**} & FPS & 0.82 & 0.18 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**sun moon lake**} & FPS & \(-1.00\) & - \\ \cline{2-4}  & Situation & Neg, unique & - \\ \hline \multirow{2}{*}{**observation deck**} & FPS & 0.67 & 0.33 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**pocket wifi**} & FPS & 0.82 & 0.18 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**credit card**} & FPS & 0.73 & 0.27 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**theme park**} & FPS & 0.60 & 0.40 \\ \cline{2-4}  & Situation & Pos, below average & Pos, below average \\ \hline \multirow{2}{*}{**qr code**} & FPS & 1.00 & - \\ \cline{2-4}  & Situation & Pos, unique & - \\ \hline \multirow{2}{*}{**day tour**} & FPS & 0.73 & 0.27 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**bus**} & FPS & 0.54 & 0.46 \\ \cline{2-4}  & Situation & Pos, below average & Pos \\ \hline \multirow{2}{*}{**sim card**} & FPS & 0.60 & 0.40 \\ \cline{2-4}  & Situation & Pos, below average & Pos, below average \\ \hline \multirow{2}{*}{**high speed**} & FPS & 0.29 & 0.71 \\ \cline{2-4}  & Situation & Pos, below average & Pos, above average \\ \hline \multirow{2}{*}{**food court**} & FPS & 0.27 & 0.73 \\ \cline{2-4}  & Situation & Pos, below average & Pos, above average \\ \hline \multirow{2}{*}{**souvenir shop**} & FPS & 0.62 & 0.38 \\ \cline{2-4}  & Situation & Pos, above average & Pos, below average \\ \hline \multirow{2}{*}{**rail bike**} & FPS & 1.00 & - \\ \cline{2-4}  & Situation & Pos, unique & - \\ \hline \multirow{2}{*}{**cable car**} & FPS & 1.00 & - \\ \cline{2-4}  & Situation & Pos, unique & - \\ \hline \multirow{2}{*}{**front desk**} & FPS & 0.29 & 0.71 \\ \cline{2-4}  & Situation & Pos, below average & Pos, above average \\ \hline \multirow{2}{*}{**hot spring**} & FPS & - & 1.00 \\ \cline{2-4}  & Situation & - & Pos, unique \\ \hline \multirow{2}{*}{**sky lantern**} & FPS & 0.43 & 0.57 \\ \cline{2-4}  & Situation & Pos, below average & Pos \\ \hline \multirow{2}{*}{**mrt station**} & FPS & 0.08 & 0.92 \\ \cline{2-4}  & Situation & Neu, below average & Pos, above average \\ \hline \multirow{2}{*}{**rock formation**} & FPS & 0.17 & 0.83 \\ \cline{2-4}  & Situation & Pos, below average & Pos, above average \\ \hline \multicolumn{2}{c}{**FPS AVG.**} & 0.51 & 0.52 \\ \hline \end{tabular}
\end{table} 
Table 2: FPS and category of each feature (\(\sigma=0.1\)). In this study, VADER was used to analyze the sentiment polarity of user reviews.