# A Lexicon-Based Framework for Mining and Analysis of Arabic Comparative Sentences
## 2 Related Work  
There were many papers that were proposed in the field of mining and analysis of comparative sentences in different languages. For example, a set of recent papers were proposed to address comparative sentences in English [9, 10], Korean [11], Chinese [12] and Vietnamese [5] languages. Since the focus of our work is on Arabic language [2], this section will only present the work proposed in this language, as will be discussed later. [PERSON] [1] mined comparative sentences in Arabic text. Firstly, Arabic comparative sentences were identified from non-comparative ones using Part Of Speech (POS) tags [15], in which the f-measure was on average 63.73% with low precision. Secondly, three machine learning classifiers were applied, namely K-nearest neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes [16]. The best obtained f-measure was 86.63% using KNN [17]. This work showed that using machine learning is much better than using POS in identifying Arabic comparative sentences. Finally, a combination of SVM and POS was applied which resulted in f-measure of 88.87%. This resulted in little improvement compared to using machine learning only. Moreover, the work in this paper applied another additional task which was the generation of a set of rules to characterize three types of Arabic comparative sentences namely: non-equal gradable, equative and superlative. However, as opposed to our work, a fourth type, namely a non-gradable comparative type, was not investigated in this work. [PERSON], and [PERSON] [3] used decision tree classifier C4.5 (J48 implementation) [18] to identify Arabic comparative opinions. Although the obtained results were promising, the combination of J48 implementation, keywords, and POS improved the performance more than the performance obtained using only J48 implementation. The keyword classifier was found to be the best technique for detecting gradable comparisons. However, the combination between the three different approaches achieved a good performance and balance between the gradable and non-gradable comparative types. [PERSON] et al. [4] proposed work in comparative relation extraction for Arabic language. In this work, the authors addressed comparative relations in MSA, Egyptian and Khaliji Arabic dialects. The Conditional Random Field (CRF) Algorithm [19, 20] was used to extract comparative relations and it achieved high accuracy results in extracting the two entities which are compared against each other in any comparative sentence. [PERSON] et al. [6] proposed a machine learning technique to identify preferred entities in Arabic comparative opinions. Five main categories were proposed to classify comparison keywords, in order to facilitate the analysis of comparative sentences. The obtained results of identification were an average f-measure of 96.5%. [PERSON] et al. [13] proposed a deep neural-network based model for the identification of comparative sentences from Arabic social media text. This work implemented the proposed model into three steps. Firstly, the data were processed to be transformed into a useful format. Secondly, the pre-processed data were fed to the model for classification. Finally, a parameter optimizer algorithm was employed for fine tuning the parameters involved in the model to enhance results. The proposed model was evaluated by two standard datasets namely Coprus and Corpus+. The obtained results showed high accuracy, precision, recall and F-score which ranged between 94% and 98%. The proposed framework is based on an Arabic comparison keyword lexicon, which was manually built for the purpose of operating and evaluating this framework. The evaluation results showed that high accuracy was obtained in all Arabic comparative opinion analysis steps. To the best of the authors' knowledge, this work presents the first comprehensive research work which implements all steps of the Arabic comparative sentences mining and analysis in which all types of Arabic comparative sentences are investigated.