# A New Feature Selection Scheme for Emotion Recognition from Text
## 4 Results and Discussion
### Results
Their contribution has been effective in five categories out of seven. It can be argued that the proposed selection scheme is more effective than Chi-Square because it selects frequent terms initially. More specifically, the terms that it selects are \begin{table}
\begin{tabular}{c c c c c} \hline  & **Chi-Square** & **Gini-Text** & **Delta** & **Relevance Score** \\ \hline
**Anger** & 87.85(200) & 87.45(50) & 87.93(50) & **87.99(50)** \\ \hline
**Disgust** & **89.72(1000)** & 89.25(1000) & 89.38(600) & 89.47(700) \\ \hline
**Fear** & 91.32(1000) & 91.50(1000) & **91.57(1000)** & 91.47(600) \\ \hline
**Guit** & 88.40(200) & 88.45(900) & **88.50(1000)** & 88.46(50) \\ \hline
**Joy** & **90.48(800)** & 90.00(1000) & 90.06(1000) & 90.02(400) \\ \hline
**Sadness** & 90.07(1000) & 90.07(1000) & **90.11(500)** & **90.11(800)** \\ \hline
**Shame** & 88.73(1000) & 88.76(50) & 88.76(50) & **88.77(200)** \\ \hline \end{tabular}
\end{table} 
Table 2: Best and second best accuracies and the number of terms used in ISEAR. Figure 1: Accuracy results in the International Survey on Emotion Antecedents and Reaction (ISEAR) dataset. (**a**) Anger, (**b**) disgust, (**c**) fear, (**d**) guilt, (**e**) joy, (**f**) sadness, and (**g**) shame. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Anger** & **Disgust** & **Fear** & **Guilt** & **Joy** & **Sad** & **Shame** \\ \hline
**Average Number of Terms** & 22 & 20 & 21 & 21 & 18 & 18 & 20 \\ \hline
**Average Number of Distinct Terms** & 19 & 19 & 18 & 18 & 15 & 16 & 17 \\ \hline \end{tabular}
\end{table} 
Table 3: Average number of terms and distinct terms per document in ISEAR. more common than the terms that are selected by Chi-Square owing to the greater A and C values as presented in Figure 2 and Table 4. Moreover, it makes a better selection than Gini-Text since most of the terms that it selects are located nearby the C axis and they are comparatively more discriminative than the terms that are selected by Gini-Text. As a result, Relevance Score selections may create better document representations during training and testing when a low number of topmost terms are utilized by providing a better trade-off between occurrence frequencies of terms and class distributions of terms in selecting features compared to Chi-Square and Gini-Text. The negative class is made of a large number of documents from diverse categories in the ISEAR dataset due to the nature of OAA approach. Authors in a previous study argue that negative class indicative terms can never reach the same maximum Chi-Square score as positive class indicative terms due to the nature of the class imbalance problem [61]. Their remarks agree with plots in Figure 2 and numbers in Table 4. The topmost 50 terms in the Chi-Square selection are commonly positive class indicative terms as they are closer to the horizontal axis. As a result, this will lead to a limited number of negative class indicative terms since many terms that are not selected are negative class indicative terms and this may lead to poor performance results for Chi-Square at extreme filtering levels. In a previous study, the authors show that Gini-Text scores of rare features are low irrespective of their distribution among positive class and negative class [49]. Their observations agree with our findings. In six categories out of seven, Chi-Square performs better than Gini-Text when the topmost 50 terms are considered. Therefore, comparatively rare features that are not selected (or, excluded) by Gini-Text but distributed asymmetrically between positive class and negative class contribute to the classification performance. Moreover, when additional terms are selected, Chi-Square selects more balanced feature subsets in terms of document frequencies (A + C) and asymmetric class distributions compared to other schemes. In the categories disgust and joy, the Accuracy performances of Chi-Square is superior to other schemes.