Abstract Topic Identifi cation is one of the important keys for the success of many applications. Indeed, there are few works in this fi eld concerning Arabic language because of lack of standard corpora. In this study, we will provide directly comparable results of six text categorization methods on a new Arabic corpus Alwatan-2004. Hence, Topic Unigram Language Model (TULM), Term... Topic Identifi cation is one of the important keys for the success of many applications. Indeed, there are few works in this fi eld concerning Arabic language because of lack of standard corpora. In this study, we will provide directly comparable results of six text categorization methods on a new Arabic corpus Alwatan-2004. Hence, Topic Unigram Language Model (TULM), Term Frequency/Inverse Document Frequency (TFIDF), Neural Network, SVM, M-SVM and TR have been experimented, and showed that TR-Classifi er is the most effi cient among the set of classifi ers, nevertheless, only binary SVM outperformed it thanks to its characteristics. Moreover, we should note that the size of Alwatan-2004 corpus used to achieve our experiments is considered the most important compared to any other Arabic corpus which had been used for topic identifi cation experiments until now. In addition, we aim through using small sizes of vocabularies to reduce the time of computation. This is important for adaptive language modeling, particularly Topic Adaptation, which is required in real time applications such as speech recognition and machine translation systems. Our experiments indicate that the results are better than other works dealing with Arabic text categorization. Read More Read Less

Abstract This paper proposes an effi cient and effective “Locality Preserving Mapping” scheme that allows text databases representatives to be mapped onto a global information retrieval system such as Peer-to-Peer Information Retrieval Systems (P2PIR). The proposed approach depends on using Locality Sensitive Hash functions (LSH), and approximate min-wise independent permutations to achieve such task. Experimental evaluation over real data, along with... This paper proposes an effi cient and effective “Locality Preserving Mapping” scheme that allows text databases representatives to be mapped onto a global information retrieval system such as Peer-to-Peer Information Retrieval Systems (P2PIR). The proposed approach depends on using Locality Sensitive Hash functions (LSH), and approximate min-wise independent permutations to achieve such task. Experimental evaluation over real data, along with comparison between different proposed schemes (with different parameters) will be presented in order to show the performance advantages of such schemes. Read More Read Less

Abstract Sentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text thus helping people to discover valuable information from large amounts of unstructured data. Sentiment analysis can be used for grouping search engine results, analyzing news content, reviews for books, movie, sports, blogs, web forums, etc. Several methods have been proposed for sentiment analysis at word, sentence, and... Sentiment analysis seeks to characterize opinionated or evaluative aspects of natural language text thus helping people to discover valuable information from large amounts of unstructured data. Sentiment analysis can be used for grouping search engine results, analyzing news content, reviews for books, movie, sports, blogs, web forums, etc. Several methods have been proposed for sentiment analysis at word, sentence, and document levels mostly based on common machine learning techniques. In this paper we explore a new methodology for sentiment analysis by considering a new set of features based on word proximities in a written text. We propose three proximity-based features, namely, proximity distribution, mutual info rmation between proximity types, and proximity patterns. We applied this approach to the analysis of movie reviews, drug reviews, and music reviews. Our experimental results show that proximity-based sentiment analysis is able to extract sentiments from a specific domain, with performance comparable to the state-of-the-art. To the best of our knowledge, this is the first attempt at focusing on only proximity-based features as the primary cues in sentiment analysis. Read More Read Less

Abstract The usability and the prime value of a software product increases proportionately with the number of different types of users supported and the number of different types of services provided at any one time by the product. The user interface for the product plays a critical role in this context. In this paper, the authors describe a dynamically reconfi gurable... The usability and the prime value of a software product increases proportionately with the number of different types of users supported and the number of different types of services provided at any one time by the product. The user interface for the product plays a critical role in this context. In this paper, the authors describe a dynamically reconfi gurable graphical user interface for a web-based application. The user interface for this application is designed as a collection of controls. The users will not only be able to view selective information from the user interface, but also will be able to rearrange the controls on the user interface. In addition, users will also be able to introduce new controls and modify existing controls on the user interface without manually recompiling the original code thus achieving greater fl exibility and saving time. The paper illustrates this dynamic reconfi guration of user interface through a case study on a health care application. Read More Read Less

Abstract Increasingly web is used for tracking numerous information pieces of large and complex data. Normal way of accessing these sources using web search engines has many limitations and we have enough literature to document them. This study is an enhancement to trap the discovery of targeted information pieces which are normally failed cases in search engines. We use an integrated... Increasingly web is used for tracking numerous information pieces of large and complex data. Normal way of accessing these sources using web search engines has many limitations and we have enough literature to document them. This study is an enhancement to trap the discovery of targeted information pieces which are normally failed cases in search engines. We use an integrated approach of conventional web search and semantic web by carefully constructed algorithms. The algorithms are tested to track data about the gulf institutions. The experiments provide encouraging results and enable us to draw future directions of research in this domain. Read More Read Less