Sentiment analysis of islamic news data using hyper-concepts

Sentiment Analysis is the extraction of writers feeling from a written manuscript. It aims at predicting the sentiment of a particular text using automated means. There are two main ways to make predictions: (1) lexicon-based techniques; (2) machine learning based techniques. Our paper contributes in the machine learning techniques, which are more accurate. Furthermore, we have adopted a hyper-conceptual method as our primary feature extraction technique. This method extracts the keywords in a hierarchical ordering of importance. Classification is then performed using the Random Forest classifier that predicts the sentiment of each document. We were able to obtain an accuracy of 90% on an comments collected from Al Jazeera News website.Qatar National Research Fund, QNRF& Qatar Foundation, QFScopu

Using conceptual reasoning for inconsistencies detection in islamic advisory opinion (Fatwas)

The Islamic websites play an important role in disseminating Islamic knowledge and information about Islamic ruling. Their number and the content they provide is continuously increasing which require in-depth investigations in content evaluation automation. In this paper, we are proposing the use of conceptual reasoning for detecting inconsistencies in case of Fatwas evaluation. Inconsistencies are detected from propositional logic point-of-view based on Truth table binary relation.Scopu

Inconsistency detection in Islamic advisory opinions using multilevel text categorization

Inconsistency detection is a large research area that has many applications. In the scope of Islamic content mining, this topic is of a particular interest because of the continuously increasing content and the need of people to find out more about its authenticity. Inconsistency detection is usually performed using linguistic analysis as well as the application of logic rules. We propose here a new method for inconsistency detection based on multilevel text categorization. For each categorization level, discriminative keywords are extracted using the hyper rectangular decomposition method which outputs the keywords in a hierarchical rank of importance. Then, those keywords are fed into the random forest classifier which automatically detects the category of each advisory opinion. Inconsistency detection is performed using an algorithm that detects inconsistent paths of advisory opinions. This study has been validated on a set of Islamic advisory opinions related to vows. The results are very interesting and show that our method is very promising in the field.This contribution was made possible by NPRP grant 06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu