# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 3 Proposed Framework
### Arabic Comparative Sentence Type Identification
The evaluation results of the REACS, PEEANCS and PEEASCS algorithms showed that the average results over all datasets were 97% precision for relation word extraction, 73% precision for feature extraction, 75% precision for first entity extraction, 82% precision for second entity extraction, and 65% precision for preferred entity extraction. Future work includes a set of extensions. Firstly, Arabic comparative sentences with more than one Arabic comparative keyword can be investigated. This extension requires incorporating an additional preprocessing step of a divide-and-conquer-like process. In this process, complex sentences with more than one Arabic comparative keyword should be detected and divided into a set of independent simple sentences. Each of these sentences can then be separately investigated by using the proposed framework. Afterwards, the results obtained from the investigation of each simple sentence can be combined to provide a summarized evaluation result of the complex sentence. Secondly, additional singular, plural and feminine Arabic comparison keywords can be added to the comparative keywords lexicon in addition to taking into consideration Arabic diacritics when manually classifying comparison keywords. This can be supported by the employment of Arabic language experts to formally guide this manual classification. Thirdly, Arabic features dictionary of positive and negative features can be used to detect the Arabic negation words so that the preferred entity can be correctly extracted. Fourthly, additional Arabic comparison keywords can be used to handle their corresponding comparative sentence types in the proposed approach. Fifthly, lexicons for other popular Arabic dialects like Moroccan or Iraqi can be developed to increase the universality of the proposed framework among other Arabic dialects. Finally, the proposed framework can be compared with more advanced machine and deep learning approaches [9; 10; 13; 22; 23]. Transformer-based natural language processing models like AraBERT [24; 25] can be used to take into consideration the context of the sentence, not only the comparison keyword. Conceptualization, A.H. and A.Y. ; methodology, A.H. and A.Y. ; software, A.H.; validation, A.H. and A.Y. ; formal analysis, A.H. and A.Y. ; investigation, A.H.; data curation, A.H.; writing--original draft preparation, A.H.; writing--review and editing, A.H. and A.Y. ; visualization, A.H. and A.Y. ; supervision, A.K. and A.Y. ; project administration, A.K. and A.Y. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:

## References

* (1) [PERSON] Opinion mining from Arabic comparative sentences. In Proceedings of the 13 th International Arab Conference on Information Technology ACIT, Balamand, Lebanon, 11-13 December 2012. * (2) [PERSON]; [PERSON]; [PERSON] Analysis and Mining of Arabic Comparative Sentences: A Literature Review. _IJCI Int. J. Comput. Inf._**2024**, _11_, 66-78. [CrossRef]
* (3) [PERSON]; [PERSON] Identifying comparative opinions in Arabic text in social media using machine learning techniques. _SN Appl. Sci._**2019**, \(1\), 1-13. [CrossRef]
* (4) [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Comparative relation extraction from Arabic opinions. _Int. J. Comput. Sci. Inf Secur._**2017**, _15_, 230-235. * (5) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Mining Vietnamese comparative sentences for sentiment analysis. In Proceedings of the 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), Ho Chi Minh City, Vietnam, 8-10 October 2015; IEEE. Piscataway, NJ, USA, 2015; pp. 162-167. * (6) [PERSON]; [PERSON]; [PERSON]; [PERSON] Sentiment analysis of Arabic comparative opinions. _SN Appl. Sci._**2019**, \(1\), 1-11.