## 4 A Pilot Study
### Discussion on the Annotated Data
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{Emotion} \\ \cline{2-8}  & Happiness & Love & Excitement & Sadness & Anger & Fear & No Emotion & Total No of Tweets \\  & & & & & & & (95\% CI) \\ \hline Attraction & 206 & 117 & 95 & 61 & 44 & 52 & 68 & \begin{tabular}{c} 311 \\ (65.47 \(\pm\) 4.28\%) \\ \end{tabular} \\ \hline Accommodation & 65 & 47 & 27 & 33 & 20 & 21 & 16 & \begin{tabular}{c} 113 \\ (23.79 \(\pm\) 3.60\%) \\ \end{tabular} \\ \hline Food \& Beverage & 55 & 43 & 30 & 24 & 22 & 18 & 6 & \begin{tabular}{c} 95 \\ (20.00 \(\pm\) 3.23\%) \\ \end{tabular} \\ \hline Weather & 66 & 45 & 43 & 22 & 16 & 18 & 11 & \begin{tabular}{c} 94 \\ (19.79 \(\pm\) 2.92\%) \\ \end{tabular} \\ \hline People \& Culture & 59 & 43 & 32 & 28 & 27 & 22 & \begin{tabular}{c} 72 \\ (15.16 \(\pm\) 1.57\%) \\ \end{tabular} \\ \hline Transport & 43 & 29 & 20 & 18 & 11 & 14 & 13 & \begin{tabular}{c} 67 \\ (14.11 \(\pm\) 4.28\%) \\ \end{tabular} \\ \hline Holiday in & 43 & 23 & 23 & 18 & 14 & 11 & 5 & \begin{tabular}{c} 57 \\ (12.00 \(\pm\) 3.83\%) \\ \end{tabular} \\ \hline Shopping \& Gift & 12 & 13 & 8 & 4 & 3 & 2 & 5 & \begin{tabular}{c} 23 \\ (4.84 \(\pm\) 3.60\%) \\ \end{tabular} \\ \hline City & 10 & 9 & 12 & 4 & 4 & 3 & 1 & \
\begin{tabular}{c} 15 \\ (3.16 \(\pm\) 3.23\%) \\ \end{tabular} \\ \hline Other targets & 12 & 6 & 4 & 3 & 2 & 3 & 38 &
\begin{tabular}{c} 51 \\ (10.74 \(\pm\) 2.92\%) \\ \end{tabular} \\ \hline Total No of tweets & 278 & 163 & 126 & 96 & 70 & 71 & 141 \\ tweets & (58.53 \(\pm\) (34.32 \(\pm\) (26.53 \(\pm\) (3.97\%)) & (20.21 \(\pm\) (14.74 \(\pm\) (14.95 \(\pm\) (29.68 \(\pm\) 4.11\%)) \\ \end{tabular} \\) & 4.27\%) & 3.97\%) & 3.61\%) & 3.19\%) & 3.21\%) & 4.11\%) \\ \hline \end{tabular} Note: Total number of tweets adds up to more than 100% because some posts belong to multiple target/emotion categories. For instance, a post on a restaurant in a hotel relates to both the food and beverage and the accommodation categories. Similarly, the analysis identified more than one emotion from some Twitter posts.  
\end{table} 
Table 3: Sentiment polarity of targets detected from the tweets based on the proposed annotation scheme. \begin{table}
\begin{tabular}{l c c c c} \hline \hline Target & Positive & Negative & Neutral & Total \\ \hline Attraction & 144 & 13 & 154 & 311 \\ \hline Food \& Beverage & 40 & 8 & 47 & 95 \\ \hline Accommodation & 47 & 7 & 59 & 113 \\ \hline Holiday in general & 29 & 3 & 25 & 57 \\ \hline People \& Culture & 43 & 9 & 20 & 72 \\ \hline Transport & 23 & 9 & 35 & 67 \\ \hline Weather & 43 & 3 & 48 & 94 \\ \hline Shopping \& Gift & 11 & 2 & 10 & 23 \\ \hline City Facilities & 7 & 2 & 6 & 15 \\ \hline Other & 28 & 1 & 22 & 51 \\ \hline Overall & 279 & 28 & 168 \\ (95\% CI) & (58.73\% \(\pm\) 4.43\%) & (5.89\% \(\pm\) 2.12\%) & (35.37\% \(\pm\) 4.30\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Targets vs. emotions detected from tweets based on the proposed annotation scheme. Figure 4: Targets and emotions detected from tweets based on the proposed annotation scheme. Figure 5: Sentiment of the targets detected from the tweets based on the proposed annotation scheme. Figure 6: Sentiment of the targets and emotions detected from the tweets based on the proposed annotation scheme. Note: the automatic system generated results in a range of \(-1\) to \(+1\). We converted the results to the range of \(-5\) and \(+5\) to align with the results of our manual annotation. From Figure 4, it is evident that targets were associated with certain types of emotion. For example, a large number of posts classified as attraction target were associated with happiness. The same observation can also be seen from other targets. This means people generally expressed and shared their happiness with others on our data.