# A New Feature Selection Scheme for Emotion Recognition from Text
## 4 Results and Discussion
### Results  
Plots report results from the experiments. Each plot in Figure 1 has four curves. They depict the experimental results obtained by the feature selection schemes. The first set of results reflects the Accuracy performances of Chi-Square, Gini-Text, Delta feature selection schemes, and the new scheme Relevance Score in the ISEAR dataset as seen in Figure 1. Relevance Score achieved the best accuracies compared to the other schemes in three categories as depicted in Table 2. Similarly, Delta achieved the best accuracies compared to others in three categories. Chi-Square performed better than others in two categories. Gini-Text has not shown superior results in any of the categories. Additionally, most selection schemes are producing improved results in disgust, fear, joy, and sadness when the numbers of features are increased. In contrast, Accuracy results are not increasing dramatically in anger, guilt, and joy. This suggests that there is a limited number of discriminative terms in those categories. The performance of Gini-Text decreases consistently in all categories as the number of selected terms decreases. On the contrary, Relevance Score shows consistently better performances when there are small numbers of features. \begin{table}
\begin{tabular}{c} When I was driving home after several days of hard work, there à was a motorist ahead of me who was \\ driving at 50 km/hour and à refused, despite his low speeded to let me overtake. \\ \end{tabular} \\ \hline When wa drive home after sever dai of hard work there wa motorist ahead of me who wa drive at km hour \\ and refus despit hi low speeded to let me overtak \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample sentence from the anger category before and after stemming. To further evaluate the results using the proposed scheme, a comparison of the best scores and second best scores obtained using the conventional feature selection approaches and the new scheme is presented in Table 2 where the bold figures and the underlined figures reflect the best and the second best Accuracy obtained in each row and the figure in brackets is the number of the selected features. The new scheme produced the best performances in three categories. Nevertheless, the number of features to obtain the best performance varies. The best performances of Chi-Square are obtained using 800 and 1000 features in joy and disgust categories. The best scores recorded in anger and shame categories are obtained using Relevance Score with only 50 and 200 features. The best performances of Delta are obtained using 1000 features in fear and guilt categories. In order to investigate the impact of binary term weighting, the average number of terms in ISEAR dataset is computed as seen in Table 3. The first rows in the table show the average number of terms per document where the local term frequencies of existing terms are summed up. The second row in the table shows the average number of distinct terms per document where each term is counted once, and its re-occurrence is neglected. The numbers are very close. This suggests that many terms occur only one time in the documents they exist in. Binary term weighting can be considered as the most suitable choice due to the low number of re-occurrences of terms in the documents. Moreover, Table 3 indicates that emotion documents are considerably shorter than documents used in other domains due to the fact that emotions are usually expressed in a single sentence. In ISEAR, when the topmost fifty features are considered, Chi-Square tends to select terms with a low number of occurrences whereas Gini-Text tends to pick highly frequent terms in the dataset as shown in Figure 2 and Table 4 where A + C is the number of documents that a term exists in. The proposed Relevance Score chooses features with a moderate number of occurrences. Nevertheless, these features occur mostly in the negative class due to the fact that they reside close to the vertical axis with greater C values and relatively smaller A values.