# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 3 Proposed Framework
### Arabic Comparative Sentence Type Identification
* The Arabic word _equi_ (but or however) may complicate some Arabic sentences and provide them with two different sentiments in the same time. Ana example of this is the Arabic sentence _equi_ (mobile A is better than mobile B, but mobile B has better battery). In this sentence, two comparative sentences, i.e., _equi_ (and _equi_ (and _equi_ (and _equi_ (are are combined together. The ACSI algorithm can identify each of these two sentences per se; however, it cannot detect the relation between them. * Some Arabic comparison keywords do not indicate a comparison at all depending on the context of the sentence. For example in the sentence _equi_ (there is nobody in place), the word () means in English, nobody, not the sharpest like in the sentence (_equi_ (this word is one of the sharpest swords). In the later sentence, the word () (sharpest) is considered a comparison keyword while in the former sentence, it is not. Such limitation also faces the application of the ACSI algorithm. * Some Arabic comparative keywords cannot detected by ACSI algorithm because of extra characters added to the keyword. For example, the superlative comparative sentence _equi_ (a prize will be given to the best student in the school), cannot be truly identified using the ACSI algorithm. The reason for this is the presence of the comparison keyword (to the best) by adding the Arabic character (to) to it. This limitation can be resolved by adding comparison keywords like _equi_ (to the best, to the developed lexicon. * The exclamation Arabic sentence can be falsely identified as a comparative sentence such as 1, _equi_ (how quickly is getting down!). The presence of the exclamation symbol at the end of the sentence is not taken into account when applying the ACSI algorithm. ### Evaluation of ACSTI Algorithm

This section discusses the evaluation results of the proposed ACSTI algorithm. Figure 3 and Table 6 show the evaluation results of the ACSTI algorithm for each comparative sentence type using each of the three considered datasets. The evaluation results of the ACSTI algorithm were calculated using Equations (1) to (4). The TP and FP parameters mentioned in the equations indicate the number of comparative sentences of a certain type that were truly and falsely identified of this type using the proposed ACSTI algorithm, respectively. The TN and FN parameters indicate the number of comparative sentences of a certain type that were truly and falsely identified as a different type using the proposed ACSTI algorithm, respectively. Each comparative sentence type has a separate subfigure shown in Figure 3. As shown in Figure 3a-d, for all sentence types in all datasets, the average precision is 96%, the average recall of the ACSTI algorithm is 91%. the average F-score of the ACSTI algorithm is 92% and the average accuracy is 96%. A detailed discussion of the results shown in this figure will be presented in the next subsections. #### 5.2.2 Discussion of Superlative Sentence Type Results

As shown previously in Figure 3, the precision of proposed ACSTI algorithm for superlative sentence type is low in some datasets. This is due to some limitations in the Arabic language which may lead to such false identification results. For example, the Arabic superlative comparison keywords (() (the best) cannot be detected as comparative keywords if Arabic diacritics are not considered when analyzing the following superlative comparative sentence (() ([PERSON] is the best intelligent student). Also, in some Arabic sentences, the non-equal comparative sentence type is falsely identified as a superlative comparative sentence type. For example, the sentence () ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (

### Evaluation of REACS, PEEANCS and PEEASCS Algorithms

This section presents the evaluation results of the proposed REACS, PEEANCS, and PEEASCS algorithms for the different comparative sentence types. Figure 4 and Table 7 show the precision of the three algorithms using each of the three considered datasets, respectively.