# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 4 Results
### Hypothesis Testing and Significance of the Emotional Features  
The conducted statistical analysis of the emotional features to assure its significance in the collected tweets is depicted in Table 7. The entries of Table 7 present a summary of the feature statistics in the data records, including \(N\) (the number of data records), the mean, standard deviation (\(SD\)), the maximum, and minimum, the 25 th percentile (Q1), \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline LSTM & 88.41 \(\pm\) 0.67 & 86.96 \(\pm\) 0.75 & 87.47 \(\pm\) 0.67 & 88.14 \(\pm\) 0.27 \\ BiLSTM & 91.21 \(\pm\) 0.74 & 90.86 \(\pm\) 0.41 & 90.96 \(\pm\) 0.96 & 91.43 \(\pm\) 0.17 \\ CNN & 88.75 \(\pm\) 0.56 & 87.34 \(\pm\) 0.63 & 86.41 \(\pm\) 0.38 & 84.74 \(\pm\) 0.68 \\ DNN & 97.17 \(\pm\) 0.48 & 96.73 \(\pm\) 0.91 & 97.18 \(\pm\) 0.19 & 96.07 \(\pm\) 0.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation measures of the used deep learning models fed with the combined (Word2 vec + emotional) features. The measures are presented as average percentages. Figure 3: Bar graph of the F1-score of the used machine and deep learning models trained using the Word2 vec and emotional combined feature set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline SVM & 80.15 \(\pm\) 0.85 & 80.69 \(\pm\) 0.21 & 74.37 \(\pm\) 0.96 & 78.48 \(\pm\) 0.65 \\ LR & 78.17 \(\pm\) 0.73 & 59.67 \(\pm\) 0.09 & 61.36 \(\pm\) 0.19 & 68.48 \(\pm\) 0.54 \\ NB & 82.18 \(\pm\) 0.38 & 59.64 \(\pm\) 0.63 & 54.19 \(\pm\) 0.37 & 56.90 \(\pm\) 0.71 \\ KNN & 79.15 \(\pm\) 0.81 & 79.37 \(\pm\) 0.94 & 79.16 \(\pm\) 0.09 & 76.38 \(\pm\) 0.73 \\ GB & 83.28 \(\pm\) 0.54 & 84.08 \(\pm\) 0.66 & 84.18 \(\pm\) 0.75 & 86.06 \(\pm\) 0.77 \\ DT & 77.17 \(\pm\) 0.19 & 78.85 \(\pm\) 0.84 & 78.25 \(\pm\) 0.07 & 75.37 \(\pm\) 0.96 \\ LDA & 78.27 \(\pm\) 0.21 & 78.18 \(\pm\) 0.29 & 77.39 \(\pm\) 0.74 & 75.69 \(\pm\) 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation measures of the used machine learning models fed with the combined (Word2 vec + emotional) features. The measures are presented as average percentages. and the 75 th percentile (Q3). The distribution of that features is explained by the aid of sketching the histograms, and boxplots, as depicted in Figures 4 and 5. A histogram is a bar chart of a numeric variable divided into bins, with the height of each bin indicating the number of instances that fall into it. It is utilized to obtain a sense of an attribute's distribution. As shown in Figure 4, the majority of the variables have Gaussian distribution. The box in the boxplot represents the middle half of the data points, the line represents the median, and the whiskers of the plots represent the feature's reasonable range. Any dots outside of the whiskers are likely to be outliers. The correlation between all features and the prediction outcome is illustrated in Figure 6. The anger feature demonstrates a high correlation with the anxiety, disgust, and sadness features; however, the joy feature reveals a high correlation with anticipation, surprise, and trust features. As illustrated in Figure 6, the outcome does not have a high correlation with the emotional feature. The correlation coefficient is a measure of how closely two continuous variables are related. However, in this study, the response variable is categorical (aggressive and non-aggressive), and the input features are continuous. So, the association between the input features and the response can be extracted using the logistic regression [56]. Binomial logistic regression is as an example of a classification technique to predict a categorical response. Logistic regression, in an instance, estimates the probability that an emotional feature belongs to a specific outcome (aggressive and non-aggressive).