# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 3 Proposed Framework
### Arabic Comparative Sentence Type Identification
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline
**Dataset** & **Sentence Type** & **Relation Word** & **Feature** & **Entity1** & **Entity2** & **Preferred Entity** \\ \hline \multirow{4}{*}{Twitter} & Non-equal Gradable & 88.89 & 8.33 & 52.78 & 77.78 & 58.33 \\  & Equative & 100 & 88.89 & 47.22 & 25 & N/A \\  & Superlative & 82.79 & 31.36 & 52.01 & N/A & 50.67 \\  & Non-gradable & 100 & N/A & 62.07 & 96.55 & N/A \\ \hline \multirow{4}{*}{MSA} & Non-equal Gradable & 100 & 95.83 & 100 & 100 & 87.5 \\  & Equative & 100 & 100 & 100 & 83.33 & N/A \\  & Superlative & 100 & 68.97 & 86.21 & N/A & 86.21 \\  & Non-gradable & 100 & N/A & 100 & 100 & N/A \\ \hline \multirow{4}{*}{Social Media} & Non-equal Gradable & 100 & 90 & 85 & 95 & 55 \\  & Equative & 100 & 90 & 70 & 60 & N/A \\ \cline{1-1}  & Superlative & 95.6 & 82.5 & 41.9 & N/A & 50.6 \\ \cline{1-1}  & Non-gradable & 100 & N/A & 100 & 100 & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Precisionof REACS, PEEANCS, and PEEASCS algorithms for each comparative sentence type. #### 5.3.2 Discussion of PEEANCS and PEEASCS Evaluation Results

This section discusses some of the limitations of extracting the preferred entity from the Arabic comparative sentence. The correct extraction of the preferred entity has a big challenge, which depends on the meaning of the relation keyword and the extracted feature. For example, in the two sentences, (the most beautiful place) and (the most ugly place), the word (place) is the entity 1. This word represents the preferred entity in the former sentence, while it is not the preferred entity in the later sentence. This is because the feature in the former sentence is a positive word, i.e., (most beautiful), while in the later sentence, the feature is a negative word, i.e., (ugly), although the relation keyword, i.e., (most) in both sentences has a positive sentiment. Another limitation of the correct extraction of the preferred entity from the Arabic comparative sentence is the presence of the Arabic negation words, Such words convert entity 1 from a preferred entity to a non-preferred one. This occurs although the relation keyword has a positive sentiment. For example, in the sentence (not better than), the negation word (not) affects the correct extraction of entity 1 as the preferred entity. ## 6 Conclusions and Future Work

This paper proposed a lexicon-based framework for the detailed analysis of Arabic comparative sentences. The proposed framework comprises a set of proposed algorithms for identifying comparative sentences, identifying comparative sentences types, extracting relation components and extracting preferred entity from Arabic comparative sentences. The proposed algorithms were abbreviated as ACSI, ACSTI, REACS, PEEANCS and PEEASCS. The proposed framework was evaluated using three Arabic datasets; one of them is publicly available, while two of them were manually developed for this purpose. The proposed framework leverages a lexicon of Arabic comparative keywords that was specifically developed to operate and evaluate the framework. This lexicon contains 649 Arabic comparative keywords that covers all the Arabic comparative sentence types. The evaluation metrics used in the experiments were the four standard metrics, namely precision, recall, F-score, and accuracy. The average accuracy value of the proposed ASCI algorithm was 97% across all datasets. The ACSI algorithm evaluation results showed that the average values of the four evaluation metrics over all datasets were in the range of 92% to 97%. The ACSTI algorithm evaluation results showed that the average values of the evaluation metrics of the proposed type identification algorithm over all comparative sentence types using all datasets were in the range of 91% to 96%.