Arabic Opinion Mining Using a Hybrid Recommender System Approach

Recommender systems nowadays are playing an important role in the delivery of services and information to users. Sentiment analysis (also known as opinion mining) is the process of determining the attitude of textual opinions, whether they are positive, negative or neutral. Data sparsity is representing a big issue for recommender systems because of the insufficiency of user rating or absence of data about users or items. This research proposed a hybrid approach combining sentiment analysis and recommender systems to tackle the problem of data sparsity problems by predicting the rating of products from users reviews using text mining and NLP techniques. This research focuses especially on Arabic reviews, where the model is evaluated using Opinion Corpus for Arabic (OCA) dataset. Our system was efficient, and it showed a good accuracy of nearly 85 percent in predicting rating from review

Evaluation of Lexical Cohesion Algorithms for Arabic Topic Segmentation

The need of having a topic segmentation system for Arabic text is due essentially to improve the functionalities of Arabic Information Retrieval (AIR). Topic segmentation of texts has been used to improve the accuracy of the subsequent processes such as question answering and information retrieval. In this paper we present the implementation and the evaluation of two algorithms for Arabic text segmentation which are Text-Tilling and C99. We compare the quality of the outputs of the two algorithms and we evaluate the relative performance of Text Tiling algorithm with respect to another cohesion based segmenter: C99 algorithm using the classical Recall/Precision evaluation metrics and the recently introduced Reader Judgment method.Keywords:Topic Segmentation, Text Tiling algorithm, C99 algorithm, Evaluation, Arabic Language