# A New AI-Based Semantic Cyber Intelligence Agent
## 5 Discussion and Concluding Remarks
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Algorithms Used** & **Precision** & **Recall** & **F1-Score** & **Reference** \\ \hline Naive Bayes (Negative) & 0.77 & 0.80 & 0.79 & [17] \\ Naive Bayes (Positive) & 0.76 & 0.76 & 0.76 & [17] \\ Naive Bayes (Security-Oriented) & 0.94 & 0.91 & 0.93 & [17] \\ Support Vector Machine (Negative) & 0.80 & 0.80 & 0.80 & [17] \\ Support Vector Machine (Positive) & 0.78 & 0.80 & 0.79 & [17] \\ Support Vector Machine (Security-Oriented) & 0.95 & 0.94 & 0.95 & [17] \\ Maximum Entropy (Negative) & 0.81 & 0.80 & 0.80 & [17] \\ Maximum Entropy (Positive) & 0.78 & 0.80 & 0.79 & [17] \\ Maximum Entropy (Security-Oriented) & 0.96 & 0.94 & 0.95 & [17] \\ Random Forest (CySecPriv) & 0.94 & 0.61 & 0.74 & [19] \\ Random Forest (’NonExpertUser) & 0.70 & 1.0 & 0.83 & [19] \\ LDA\textbackslash{}VEM + TF-IDF (Personal) & - & - & 0.76 & [20] \\ LDA\textbackslash{}VEM + TF-IDF (Professional) & - & - & 0.67 & [20] \\ LDA\textbackslash{}VEM + TF-IDF (Health) & - & - & 0.75 & [20] \\ SVC (Cyber Bullying) & 0.73 & 0.96 & 0.83 & [21] \\ Logistic Regression (Cyber Bullying) & 0.91 & 0.96 & 0.93 & [21] \\ Multinomial Naive Bayes (Cyber Bullying) & 0.86 & 0.94 & 0.90 & [21] \\ Random Forest Classifier (Cyber Bullying) & 0.98 & 0.73 & 0.84 & [21] \\ SGD Classifier (Cyber Bullying) & 0.90 & 0.95 & 0.93 & [21] \\ Light Gradient Boosted Machine (Darknet Traffic) & - & - & 0.84 & [22] \\ Proposed (Comprehensive Cyber) & 0.91 & 0.85 & 0.88 & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance comparison with existing research in social media-based cyber intelligence. \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Actual Positive** & **Actual Negative** \\ \hline Predicted Positive & **23,178** (TP) & 2241 (FP) \\ Predicted Negative & 4149 (FN) & **7818** (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Confusion matrix in identifying relevancy of the cyber-related tweets. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithms Used** & **Precision** & **Recall** & **F1-Score** & **Reference** \\ \hline Naive Bayes (Negative) & 0.77 & 0.80 & 0.79 & [17] \\ Naive Bayes (Positive) & 0.76 & 0.76 & 0.76 & [17] \\ Naive Bayes (Security-Oriented) & 0.94 & 0.91 & 0.93 & [17] \\ Support Vector Machine (Negative) & 0.80 & 0.80 & 0.80 & [17] \\ Support Vector Machine (Positive) & 0.78 & 0.80 & 0.79 & [17] \\ Support Vector Machine (Security-Oriented) & 0.95 & 0.94 & 0.95 & [17] \\ Maximum Entropy (Negative) & 0.81 & 0.80 & 0.80 & [17] \\ Maximum Entropy (Positive) & 0.78 & 0.80 & 0.79 & [17] \\ Maximum Entropy (Security-Oriented) & 0.96 & 0.94 & 0.95 & [17] \\ Random Forest (CySecPriv) & 0.94 & 0.61 & 0.74 & [19] \\ Random Forest (’NonExpertUser) & 0.70 & 1.0 & 0.83 & [19] \\ LDA\textbackslash{}VEM + TF-IDF (Personal) & - & - & 0.76 & [20] \\ LDA\textbackslash{}VEM + TF-IDF (Professional) & - & - & 0.67 & [20] \\ LDA\textbackslash{}VEM + TF-IDF (Health) & - & - & 0.75 & [20] \\ SVC (Cyber Bullying) & 0.73 & 0.96 & 0.83 & [21] \\ Logistic Regression (Cyber Bullying) & 0.91 & 0.96 & 0.93 & [21] \\ Multinomial Naive Bayes (Cyber Bullying) & 0.86 & 0.94 & 0.90 & [21] \\ Random Forest Classifier (Cyber Bullying) & 0.98 & 0.73 & 0.84 & [21] \\ SGD Classifier (Cyber Bullying) & 0.90 & 0.95 & 0.93 & [21] \\ Light Gradient Boosted Machine (Darknet Traffic) & - & - & 0.84 & [22] \\ Proposed (Comprehensive Cyber) & 0.91 & 0.85 & 0.88 & \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance evaluations in identifying relevant cyber-related tweets.