## 6 Conclusions and Future Work  
Identifying eyewitnesses from Twitter is useful for supporting disaster relief activities. It is an active research area, and diverse methods have been proposed in the literature. In this work, we proposed a method that utilizes language structure, linguistics, and word relation to extract the feature set automatically. We proposed a generic approach that covers different disaster types. The proposed approach is evaluated on the benchmark dataset, and results are compared with a baseline static-dictionary-based approach. The evaluation results show that the proposed LR-TED approach achieved an F-score of 0.93 and competes with the high score achieved by the manually crafted dictionary-based approach in the same category. The implication of the proposed technique can be recognized when we have to process potentially millions of tweets to respond to the event at the earliest.  
The implication of this research work can be a potential contribution to various applications in this domain, such as disaster management systems, disaster or emergency alert systems, emergency response for institutes and agencies, etc. In the future, the use of manually created static dictionaries will not be required for the identification of the important feature terms from the text for eyewitness identification. Furthermore, the proposed LR-TED approach is saleable on social media platforms other than Twitter and for various disaster types.  
This research has opened a debate on adapting linguistic rules for feature extraction instead of relying on manually crafted dictionaries to automatically classify eyewitness messages. The proposed LR-TED approach has the capability to dynamically extract feature terms using linguistic rules, hence, scalable to diverse disaster types and unseen contents in the English language. The main limitation of the proposed LR-TED approach is its lack of applicability to other languages. Since each language has its own set of grammar rules and structure, it is difficult to adapt linguistic features of one language to others. In future, we plan to extend LR-TED capabilities to other languages by developing more general and language specify rules.  
Conceptualization, S.H., A.M., S.K. and M.T.A.; funding acquisition, S.K. and M.A.; methodology, S.H., A.M., S.K. and M.T.A.; project administration, A.M. and S.K.; resources, S.K. and M.A.; supervision, A.M. and M.T.A.; validation, S.H., A.M. and M.A.; writing--original draft, S.H.; Writing--review and editing, S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, project number 523.  
Not applicable.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & & **LR-TED** & & & **Zahra’s Approach** \\ \hline
**Category** & **Precision** & **Recall** & **F-Score** & **Precision** & **Recall** & **F-Score** \\ \hline Eyewitness & 0.86 & 0.95 & 0.91 & 0.40 & 0.74 & 0.52 \\ Non-Eyevitness & 0.47 & 0.47 & 0.47 & 0.39 & 0.42 & 0.40 \\ Unknown & 0.28 & 0.06 & 0.09 & 0.38 & 0.11 & 0.17 \\ \hline \hline \end{tabular}
\end{table}
Table 15: A comparative analysis of the ANN model.  
**Acknowledgments:** The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia, for funding this research work through project number 523.  
**Conflicts of Interest:** The authors declare no conflict of interest.