## 3 Materials and Methods for Manual Expansion
### Processing OOV Expressions in Hiejima's Dictionary  
[PERSON] et al. designed a questionnaire survey on the type of emotion expressed by each emoticon generated by their automatic emoticon generation algorithm [21]. In our research, to determine the most appropriate type of emotion expressed by the emotive expressions of uncertain emotion types from [PERSON]'s dictionary, we decided that it would also be appropriate to prepare a similar questionnaire survey, containing not emoticons but rather example sentences containing those OOV emotive expressions and conduct the questionnaire among native speakers of Japanese.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Emotion**} & \multicolumn{8}{c}{**Nakamura’s Dictionary**} \\ \cline{2-10}
**Categories** & **Joy** & **Fondness** & **Relief** & **Gloom** & **Dislike** & **Anger** & **Fear** & **Shame** & **Excitement** & **Surprise** & **OOV** \\ \hline \multirow{4}{*}{Joy} & 91 & 1 & & 2 & & & 4 & 48 \\  & (62.33\%) & (0.68\%) & & & (1.37\%) & & & (2.74\%) & & (32.88\%) \\ \cline{2-10}  & Love & 1 & 41 & 4 & 8 & & 1 & 1 & 116 \\  & (0.58\%) & (23.84\%) & (2.33\%) & (4.65\%) & & & (0.58\%) & & (0.58\%) & & (67.44\%) \\ \cline{2-10}  & Anger & & & & 23 & 49 & 3 & 7 & 107 \\  & & & & & (12.17\%) & (25.93\%) & (1.59\%) & & (3.70\%) & & (56.61\%) \\ \cline{2-10}  & Suftering & 1 & 4 & 74 & 2 & 8 & 5 & 54 \\  & & (0.68\%) & (2.70\%) & (50.00\%) & (1.35\%) & (5.41\%) & & (3.38\%) & & (36.49\%) \\ \cline{2-10}  & Sadness & & & 28 & 48 & 1 & 1 & 1 & 41 \\  & & & & (23.53\%) & (40.34\%) & & (0.84\%) & & (34.45\%) \\ \cline{2-10}  & Blame & 4 & & & 16 & 3 & 4 & 4 & & 161 \\  & (2.08\%) & & & (8.33\%) & (1.56\%) & (2.08\%) & (2.08\%) & & & (83.85\%) \\ \cline{2-10}  & Enjoyment & 64 & 5 & 1 & & & & & & 70 \\  & (45.71\%) & (3.57\%) & (0.71\%) & & & & & & (50.00\%) \\ \cline{2-10}  & Surprise & & & 3 & 2 & & 34 & 45 \\  & & & & (3.57\%) & (2.38\%) & & & (40.48\%) & (53.57\%) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number and percentages of existing emotive expressions.  
Figure 8: Existing emotive expressions in expressions of surprise.  
First, for each OOV word, 10 example sentences containing it were prepared. In the questionnaire, respondents participating in the survey were asked to choose the type of emotion expressed in the example sentences.  
However, as can be seen in Table 1, there were 642 OOV expressions overall. If all of these OOV expressions were to be processed using the above method, 6420 example sentences would need to have been first prepared. This would not only be burdensome in terms of preparing the dataset of example sentences but would also require a considerable amount of time to complete the questionnaire survey by the participants, resulting in fatigue and errors. Since the less time it takes to process the OOV expressions, the better, we needed to reduce the number of example sentences to 5 (since the maximum number of example sentences for a word in [PERSON]'s dictionary is 5) and reduce the number of OOV expressions in some other way to make the study realizable in the given time constraints.