## 5 Experiments and Results
### Comparison of Proposed LR-TED Approach with [PERSON]'s Approach (All Experiments)  
In this subsection, we present the complete results achieved from each model in our experiments. Table 12 illustrates the F-scores achieved using different models for each disaster type \"eyewitness\" class. Similarly, Table 13 is for the \"non-eyewitness\" class, and Table 14 is for the \"unknown\" class.  
In this work, our focus is to identify the \"eyewitness\" class and all the results discussed in the above sections are at the model level. From Table 12, for the comparison of different experiments of eyewitness class, the maximum f-scores achieved are 0.93, 0.52, 0.60, and 0.26 for earthquake, flood, hurricane, and wildfire events, respectively. As an individual approach for eyewitness class, the LR-TED approach has produced better results for three out of four disaster types and comparative results for flood disaster types. The LR-TED outperformed [PERSON]'s approach for earthquake and hurricane disaster types and produced comparative scores for the remaining two disaster types. These scores can be considered significant scores by an automated process without using a static dictionary.  
From the above experiments, LR-TED, SVM, Naive Bayes, and Random Forest algorithms performed best for eyewitness, non-eyewitness, and unknown categories, respectively. LR-TED scores remained the best among other models for each disaster type. When comparing the scores with [PERSON]'s approach, we have demonstrated that the proposed LR-TED approach performs best or achieved comparative results considering that LR-TED is a fully automatic approach without using the static dictionary as the baseline approach. After a comprehensive study and detailed experiments, we finally selected the ANN model as part of the LR-TED approach for classifying eyewitness tweets. The same observation is found in [PERSON]'s approach, where the identified features perform best for earthquake events. Table 15 shows the comparative results of precision, recall, and F-score for the ANN model based on LR-TED and [PERSON]'s approach.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**MODEL** & **Manual** & **R-F** & **N-B** & **SVM** & **LR-TED** & **RNN** & **CNN** & **Zahra** \\ \hline
**Disaster Type** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** \\ \hline Earthquake & 0.47 & 0.75 & 0.73 & 0.74 & 0.75 & 0.00 & 0.74 & 0.71 \\ Flood & 0.40 & 0.66 & 0.64 & 0.56 & 0.65 & 0.60 & 0.64 & 0.63 \\ Hurricane & 0.57 & 0.80 & 0.80 & 0.80 & 0.80 & 0.80 & 0.81 & 0.81 \\ Wildfire & 0.61 & 0.85 & 0.85 & 0.86 & 0.86 & 0.86 & 0.86 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Comparison of Results—Non-Eyevitness (All Experiments).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**MODEL** & **Manual** & **R-F** & **N-B** & **SVM** & **LR-TED** & **RNN** & **CNN** & **Zahra** \\ \hline
**Disaster Type** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** & **(F1)** \\ \hline Earthquake & 0.09 & 0.08 & 0.14 & 0.00 & 0.07 & 0.00 & 0.01 & 0.39 \\ Flood & 0.17 & 0.56 & 0.55 & 0.60 & 0.51 & 0.54 & 0.57 & 0.73 \\ Hurricane & 0.13 & 0.39 & 0.39 & 0.27 & 0.36 & 0.00 & 0.32 & 0.64 \\ Wildfire & 0.19 & 0.56 & 0.55 & 0.57 & 0.55 & 0.53 & 0.54 & 0.73 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Comparison of Results—Unknown (All Experiments).  
Since the proposed LR-TED technique automatically extracts the feature words without involving any human interaction to create and maintain the dictionary of feature words manually, hence, we can claim that the grammar rule-based LR-TED approach could be deemed a potential method for identifying eyewitness tweets, particularly in scenarios where potentially millions of tweets need to be processed quickly and for unknown events.