The word mismatch problem is fundamental to Information retrieval. Query expansion process helps to overcome this problem. Based on the Arabic corpuses, the comparisons between two query expansion techniques (global and local query) have been conducted to determine the query effectiveness. First one represents the local context analysis which represents a local method, while a global method was the second technique that has been represented by the Association and similarity thesauruses. These techniques can be used in any special field or domain to improve the expansion process and to get more relevant documents for the user's query. This study introduces a comparison between these approaches and shows their effectiveness. Although, local context analysis has some advantages over the similarity thesaurus, Association thesaurus which is global is generally the most effective one.

Arabic language is distinguished by its morphological richness, which forces the workers in the field of Arabic language Processing (i.e., information retrieval, document's classification, text summarizing) to deal with many words that seem to be different but in reality they came from an identical root word. One of the methods to overcome this problem is to return the words to their roots. This research aims to provide a new algorithm, that returns roots of Arabic words using n-gram technique without using morphological rules in order to avoid the complexity arising from the morphological richness of the language in one hand and the multiplicity of morphological rules in other hand. The proposed algorithm uses a list that contains over 4,500 identical roots words.

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Recently, Online Social Networks (OSNs) are considered as important resource of information, since they provide a huge amount of data that reflects the interactions between users in various fields, such as: politics, sport and business. Opinion mining (or sentiment analysis) is a process that uses natural language processing, and text analysis methods to understand users' feelings or opinions, and detect their polarity, which could be positive, negative or neutral. The outcomes of opinion mining approaches help in extracting useful patterns that enable traders to take critical decisions for business, marketing and politics. In the literature, we have several proposed opinion mining systems, tools and approaches, but in general they are not available on public. Many other online opinion mining tools are simple to use and available for free or as demos. Opinion mining online tools performance need to be evaluated to attract researchers and companies utilizing their advantages. The main purpose of this study is to evaluate how efficient are online opinion mining tools for Arabic language. We used benchmark Arabic opinion collections and classify them using two popular online sentiment analysis tools that support Arabic language; Paralleldots and Repustate. The experiment used prediction quality measurement to evaluate these tools and compare their results with several machine learning classifiers in order to recommend the best available solution for Arabic sentiment analysis. Our results showed that Paralleldots API is highly recommended for Arabic sentiment analysis for both positive and negative reviews.

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