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The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts is their opinions on different issues. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This work focuses on the later approach due to its various challenges and high potential. The discussions in this paper take the reader through the detailed steps of building the main two components of the lexicon-based SA approach: the lexicon and the SA tool. The experiments show that significant efforts are still needed to reach a satisfactory level of accuracy for the lexicon-based Arabic SA. Nonetheless, they do provide an interesting guide for the researchers in their on-going efforts to improve lexicon-based SA. ENDNOTES1 Some studies include the neutral case as well. 2

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The emergence of the Web 2.0 technology generated a massive amount of raw data by enabling Internet users to post their opinions on the web. Processing this raw data to extract useful information can be a very challenging task. An example of important information that can be automatically extracted from the users' posts is their opinions on different issues. This problem of Sentiment Analysis (SA) has been studied well on the English language and two main approaches have been devised: corpus-based and lexicon-based. This work focuses on the later approach due to its various challenges and high potential. The discussions in this paper take the reader through the detailed steps of building the main two components of the lexicon-based SA approach: the lexicon and the SA tool. The experiments show that significant efforts are still needed to reach a satisfactory level of accuracy for the lexicon-based Arabic SA. Nonetheless, they do provide an interesting guide for the researchers in their on-going efforts to improve lexicon-based SA.

Text categorization or classification (TC) is concerned with placing text documents in their proper category according to their contents. Owing to the various applications of TC and the large volume of text documents uploaded on the Internet daily, the need for such an automated method stems from the difficulty and tedium of performing such a process manually. The usefulness of TC is manifested in different fields and needs. For instance, the ability to automatically classify an article or an email into its right class (Arts, Economics, Politics, Sports, etc.) would be appreciated by individual users as well as companies. This paper is concerned with TC of Arabic articles. It contains a comparison of the five best known algorithms for TC. It also studies the effects of utilizing different Arabic stemmers (light and root-based stemmers) on the effectiveness of these classifiers. Furthermore, a comparison between different data mining software tools (Weka and RapidMiner) is presented. The results illustrate the good accuracy provided by the SVM classifier, especially when used with the light10 stemmer. This outcome can be used in future as a baseline to compare with other unexplored classifiers and Arabic stemmers.

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The problem of automatically extracting opinions and emotions from textual data have gained a lot of interest recently. Unfortunately, most studies on Sentiment Analysis (SA) focus mainly on the English language, whereas studies considering other important and wide-spread languages such as Arabic are few. Moreover, publicly-available Arabic datasets are seldom found on the Web. In this work, a labeled dataset of Arabic reviews/comments is collected from a social networking website (Yahoo!-Maktoob). A detailed analysis of different aspects of the collected dataset such as the reviews' length, the numbers of likes/dislikes, the polarity distribution and the languages used is presented. Finally, the dataset is used to test popular classifiers commonly used for SA.

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