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With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user's query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.

Abstract-Today, the number of users of social network is increasing. Millions of users share opinions on different aspects of life every day. Therefore social network are rich sources of data for opinion mining and sentiment analysis. Also users have become more interested in following news pages on Facebook. Several posts; political for example, have thousands of users' comments that agree/disagree with the post content. Such comments can be a good indicator for the community opinion about the post content. For politicians, marketers, decision makers …, it is required to make sentiment analysis to know the percentage of users agree, disagree and neutral respect to a post. This raised the need to analyze theusers' comments in Facebook. We focused on Arabic Facebook news pages for the task of sentiment analysis. We developed a corpus for sentiment analysis and opinion mining purposes. Then, we used different machine learning algorithms -decision tree, support vector machines, and naive bayes -to develop sentiment analyzer. The performance of the system using each technique was evaluated and compared with others.

Sentiment analysis involves classifying opinions in text into categories like "positive" or "negative". One of approaches used to make sentiment classification is using sentiment lexicon. This paper aims to build a sentiment lexicon which is domain independent. We propose a Machine Learning Based Senti-word Lexicon (MLBSL) based on the Amazon data set which contains reviews from different domains. Our proposed MLBSL yields an improvement over previous published manual and automatic-built lexicons like SentiWordNet. We also provide an improvement in calculation method used in reviews sentiment analysis.

Across the world, several millions of people use sign language as their main way of communication with their society, daily they face a lot of obstacles with their families, teachers, neighbours, employers. According to the most recent statistics of World Health Organization, there are 360 million persons in the world with disabling hearing loss i.e. (5.3% of the world's population), around 13 million in the Middle East. Hence, the development of automated systems capable of translating sign languages into words and sentences becomes a necessity. We propose a model to recognize both of static gestures like numbers, letters, ...etc and dynamic gestures which includes movement and motion in performing the signs. Additionally, we propose a segmentation method in order to segment a sequence of continuous signs in real time based on tracking the palm velocity and this is useful in translating not only pre-segmented signs but also continuous sentences. We use an affordable and compact device called Leap Motion controller, which detects and tracks the hands' and fingers' motion and position in an accurate manner. The proposed model applies several machine learning algorithms as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Artificial Neural Network (ANN) and Dynamic Time Wrapping (DTW) depending on two different features sets. This research will increase the chance for the Arabic hearing-impaired and deaf persons to communicate easily using Arabic Sign language(ArSLR). The proposed model works as an interface between hearing-impaired and normal persons who are not familiar with Arabic sign language, overcomes the gap between them and it is also valuable for social respect. The proposed model is applied on Arabic signs with 38 static gestures (28 letters, numbers (1:10) and 16 static words) and 20 dynamic gestures. Features selection process is maintained and we get two different features sets. For static gestures, KNN model dominates other models for both of palm features set and bone features set with accuracy 99 and 98% respectively. For dynamic gestures, DTW model dominates other models for both palm features set and bone features set with accuracy 97.4% and 96.4% respectively.

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