# Topic-Specific Emotion Mining Model for Online Comments
## 4 Experiments
### Understandability of Results in Our Model  
Our method uses aspect-opinion pairs instead of bag of words to represent review texts, which makes our outputs contain more semantic information than the other models. In our model output results, opinion words and aspect words are not mixed at random, but appear in the original collocation, which will undoubtedly help one understand the results.  
The top 15 word pairs with their probability of the topic \"Crowd's positive emotion\" and that of the topic \"Package's negative emotion\" are enumerated in Table 4. Figures 10 and 11 are two associated networks, which are respectively generated from the top 31 word pairs with probability under the topic \"Crowd's positive emotion\" and the topic \"Package's negative emotion\". Apart from aspect-specific opinion, such as 'broken', general opinion words do not reflect topic information. For example, the word 'like' cannot be judged as to which aspect it describes. Similarly, a topic word does not carry any emotional information, such as 'child', 'brother', and 'box'. When the topic words and viewpoints are presented in the form of word pairs, such as \"children-like\", \"son-appreciate\", and \"whole families-favor\", it can be seen clearly that the positive emotions of the topic 'Crowd' are reflected, indicating that the product is liked by all kinds of people. Word pairs like \"packaging-breakage\", \"Box-damaged\", and \"wrapper-broken\" not only reveal consumers' dissatisfaction with the product packaging, but also show that the reason for the dissatisfaction is the damage of the packing box.  
To sum up, the TSEM model, in the form of word pairs, can exactly express more abundant semantic information, making the result of emotion mining easier for human understanding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Crowd (Positive)**} & \multicolumn{3}{c}{**Package (Negative)**} \\ \hline \multicolumn{2}{c}{**Aspect–Opinion Pairs**} & \multicolumn{1}{c}{**Probability**} & \multicolumn{1}{c}{**Aspect–Opinion Pairs**} & \multicolumn{1}{c}{**Probability**} \\ \hline children & like & 0.156 & packaging & breakage & 0.041 \\ \hline baby & love & 0.071 & box & damaged & 0.0265 \\ \hline son & appreciate & 0.0526 & wrapper & broken & 0.008 \\ \hline darling child & enjoy & 0.0385 & package & bad & 0.008 \\ \hline daughter & like very much & 0.0217 & packing box & breakdown & 0.008 \\ \hline little friends & keen on & 0.0195 & package & rotten & 0.0067 \\ \hline children & partiality & 0.0098 & case & rotten & 0.0067 \\ \hline family & really like & 0.0098 & packing case & shabby & 0.0054 \\ \hline child & great & 0.0076 & \begin{tabular}{c} external \\ packing \\ \end{tabular} & worn & 0.0054 \\ \hline whole families & favor & 0.006 & chest & damaged & 0.0054 \\ \hline brother & praise & 0.0055 & packaging & old & 0.0054 \\ \hline children & very fond of & 0.0055 & case & tight & 0.0054 \\ \hline child & like & 0.0049 & paper box & broken & 0.0041 \\ \hline folk & enjoy & 0.0049 & wrapper & \
\begin{tabular}{c} Broken and \\ rotten \\ \end{tabular} & 0.004 \\ \hline mother &
\begin{tabular}{c} particularly \\ love \\ \end{tabular} & 0.0044 & box & breakage & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Top 15 word pairs with probability of positive emotion of topic ‘Crowd’ and negative emotion of topic ‘Package’.  
Figure 11: The association network under the negative emotion of the topic ‘Package’.  
Figure 10: The association network under the positive emotion of the topic ‘Crowd’.