## 3 Data Analysis
### Label Distribution  
Label counts for our dataset are shown in Table 4. Datasets created from randomly sampled data tend to have a long-tailed label distribution. This is generally more pronounced when fine-grained labels are used. For example, in GoEmotions, the most common emotion label is \"admiration\" which has 53 times (53.64 times, measured on the train set) as many examples as the least common emotion label, \"grief\", despite the use of a \"pilot model\" to balance emotions. In our case, the most common label, \"nostalgia\" (Appendix C explains why), is only 2 times more common than the least common label, \"greed\". The balanced distribution and the large size of our dataset (200k sentences) should allow for the use of automatic pruning methods to reduce noise or sampling for the creation of manually labeled datasets.  
Table 5 shows the number of labels per example as a percentage. More than 60% of the examples have between 0 and 2 emotion labels, while only around 13% have 5 or more emotion labels. For comparison, in GoEmotions, 83% of the examples have only 1 label. This is because the authors of GoEmotions specifically optimized their taxonomy to minimize the number of commonly overlapping emotions and limit the number of emotions. In our work, we instead tried to have the highest number of emotions that we could, for a first attempt, while still being able to clearly define and separate them. This more fine-grained approach to the taxonomy is designed to enable more precise study. For example, \"despair\" might be very similar to \"sadness\" but its difference can be very significant, not just when analyzing fiction but also non-fiction. One example is medical texts, where \"despair\" can be more closely related to depression than \"sadness\". Section 3.2 deals with emotion correlation.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Train** & **Validation** & **Test** & **Gold** \\ \hline admiration & 7700 & 963 & 962 & 110 \\ amusement & 7427 & 928 & 928 & 52 \\ anger & 8556 & 1070 & 1070 & 72 \\ annoyance & 10,730 & 1341 & 1341 & 57 \\ approval & 8531 & 1066 & 1066 & 98 \\ boredom & 8113 & 1014 & 1014 & 53 \\ calmness & 8573 & 1072 & 1072 & 45 \\ caring & 8972 & 1122 & 1121 & 64 \\ courage & 8484 & 1061 & 1060 & 42 \\ curiosity & 7738 & 967 & 967 & 67 \\ desire & 10,160 & 1270 & 1270 & 81 \\ despair & 10,009 & 1251 & 1251 & 44 \\ disappointment & 12,133 & 1517 & 1517 & 39 \\ disapproval & 11,130 & 1391 & 1391 & 111 \\ disgust & 8987 & 1123 & 1123 & 72 \\ doubt & 9012 & 1127 & 1127 & 43 \\ embarrassment & 9642 & 1205 & 1205 & 22 \\ envy & 9942 & 1243 & 1243 & 14 \\ excitement & 10,794 & 1349 & 1349 & 38 \\ faith & 8442 & 1055 & 1055 & 13 \\ fear & 11,556 & 1445 & 1445 & 39 \\ frustration & 11,162 & 1395 & 1395 & 54 \\ gratitude & 11,279 & 1410 & 1410 & 14 \\ greed & 7423 & 928 & 928 & 25 \\ grief & 10,972 & 1372 & 1371 & 14 \\ guilt & 8660 & 1082 & 1082 & 13 \\ indifference & 8549 & 1069 & 1069 & 37 \\ joy & 9404 & 1175 & 1175 & 61 \\ love & 8838 & 1105 & 1105 & 50 \\ nervousness & 7747 & 968 & 968 & 24 \\ nostalgia & 14,805 & 1851 & 1851 & 29 \\ optimism & 9560 & 1195 & 1195 & 37 \\ pain & 10,014 & 1252 & 1252 & 22 \\ pride & 10,744 & 1343 & 1343 & 27 \\ relief & 9317 & 1165 & 1165 & 25 \\ sadness & 9589 & 1199 & 1199 & 52 \\ surprise & 9818 & 1227 & 1227 & 36 \\ trust & 8606 & 1076 & 1076 & 43 \\ \hline neutral & 22,803 & 2890 & 2919 & 15 \\ \hline Sentences & 160,000 & 20,000 & 20,000 & 727 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of examples per emotion for each data subset, including the human-labeled gold dataset. We nearly achieved a balanced distribution of examples per emotion label.