Heider A. Wahsheh

Opinion Mining and Analysis for Arabic Language

Social media constitutes a major component of Web 2.0 and includes social networks, blogs, forum discussions, micro-blogs, etc. Users of social media generate a huge volume of reviews and comments on a daily basis. These reviews and comments reflect the opinions of users about different issues, such as: products, news, entertainments, or sports. Therefore different establishments may need to analyze these reviews and comments. For examples: It is essential for companies to know the pros and cons of their products or services in the eyes of customers. Governments may want to know the attitude of people towards certain decisions, services, etc. Although the manual analysis of textual reviews and comments can be more accurate than the automatic methods, nonetheless, it is time consuming, expensive, and can be subjective. Furthermore, the huge amount of data contained in social networks can make it impractical to perform analysis manually. This paper focuses on evaluating Arabic social content. Currently, Middle East is an area rich of major political and social reforms. The social media can be a rich source of information to evaluate such contexts. In this research we developed an opinion mining and analysis tool to collect different forms of Arabic language (i.e. Standard or MSA, and colloquial). The tool accepts comments and opinions as input and generates polarity based outputs related to the comments. Additionally the tool can determine the comment or review is: (subjective or objective), (positive or negative), and (strong or weak). The evaluation of the performance of the developed tool showed that it yields more accurate results when it is applied on domain-based Arabic reviews relative to general-based Arabic reviews.

Sentiment analysis of arabic social media content: a comparative study

The Internet became an indispensable part of peoples lives because of the significant role it plays in the ways individuals interact, communicate and collaborate with each other. Over recent years, social media sites succeed in attracting a large portion of online users where they become not only content readers but also content generators and publishers. Social media users generate daily a huge volume of comments and reviews related to different aspects of life including: political, scientific and social subjects. In general, sentiment analysis refers to the task of identifying positive and negative opinions, emotions and evaluations related to an article, news, products, services, etc. Arabic sentiment analysis is conducted in this study using a small dataset consisting of 1,000 Arabic reviews and comments collected from Facebook and Twitter social network websites. The collected dataset is used in order to conduct a comparison between two free online sentiment analysis tools: SocialMention and SentiStrength that support Arabic language. The results which based on based on the two of classifiers (Decision tree (J48) and SVM) showed that the SentiStrength is better than SocialMention tool.

Content-based analysis to detect Arabic web spam

Search engines are important outlets for information query and retrieval. They have to deal with the continual increase of information available on the web, and provide users with convenient access to such huge amounts of information. Furthermore, with this huge amount of information, a more complex challenge that continuously gets more and more difficult to illuminate is the spam in web pages. For several reasons, web spammers try to intrude in the search results and inject artificially biased results in favour of their websites or pages. Spam pages are added to the internet on a daily basis, thus making it difficult for search engines to keep up with the fast-growing and dynamic nature of the web, especially since spammers tend to add more keywords to their websites to deceive the search engines and increase the rank of their pages. In this research, we have investigated four different classification algorithms (naïve Bayes, decision tree, SVM and K-NN) to detect Arabic web spam pages, based on content. The three groups of datasets used, with 1%, 15% and 50% spam contents, were collected using a crawler that was customized for this study. Spam pages were classified manually. Different tests and comparisons have revealed that the Decision Tree was the best classifier for this purpose.

Arabic sentiment polarity identification using a hybrid approach

Recent years witness a significant increase in research related to knowledge extraction from web social networks or media. The enormous volume of posted comments, and related media can be a rich source of information. In Middle East and the Arab world in particular, social media websites continue to be the top visited websites especially with the current social and political changes in this part of the world. Sentiment analysis and opinion mining focus on identifying and evaluating positive and negative opinions and comments. This study aims to identify the sentiment polarity for collected comments or posts from Twitter using a hybrid approach and a modest dataset of Arabic (Text and audio) comments. Two machine learning classification techniques are used to perform the required classification to identify the polarity of the collected opinions. We extended the evaluation of prediction algorithms and enhance them using Bagging and Boosting algorithms. We extracted a unified dataset of texts, audios and images and applied processing methods to extract final sentiment opinions. We noticed that some special expressions specially in recoding (such as laughing, yelling, etc. within the recording) have a negative effect on the accuracy of the automatic sentiment prediction system.

Evaluating Arabic spam classifiers using link analysis

The rank of pages within the search engine results page (SERP) is important especially for commercial sites. It is also important for those establishments which try to be at the top 10 of SERP to gain more visitors. To be within the top 10 results of SERP means also being visible to a larger number of audiences. On the other hand, gaining a lower rank means being less visible and eventually gaining less revenues relative to competitive pages which are ranked higher. Therefore the Web has a number of pages which adopt spamming techniques to deceive search engines and gains a higher rank than what they really deserve. Link-based technique is one of the techniques used to achieve this goal. This study aims to detect the link-based spamming techniques used within Arabic spam Web pages. The conducted tests reveal that link-based spamming technique is used significantly within Arabic spammed Web pages.

Mutation operators for JADE mobile agent systems

Mobile Agent System (MAS) is a distributed software system responsible for supporting and managing mobile agents. It is suitable to develop many applications for mobile computing. Testing and debugging MASs is hard to do, due the complex execution of MAS. In this paper we are interested in using mutation analysis to evaluate, compare and improve quality for MASs. There are many mutation operators in the literature. However, they are insufficient for MASs. This paper we extract 26 mutation operators for JADE MASs. Those mutation operators are categorized into four levels based on the specific fault features of JADE Mobile Agent System.

SPAR: A system to detect spam in Arabic opinions

The evaluation of the public opinion through websites, social networks, news feedback, etc. is currently getting an extensive research to discover public opinion regarding the current social and political changes in the Middle Eastern countries. However, the level of trust or confidentiality of such public opinion evaluations may have the risk of being spammed. This study aims to detect the spam opinions in the Yahoo!-Maktoob social network. The proposed system reads the opinions and classifies them into one of the following two classes: spam and non-spam opinions, based on a number of features. Each spam opinion categorizes into; high levels spam and low level spam, based on special metrics. While each non-spam opinion is labeled as; positive, negative, or neutral based on the language polarity dictionaries. Those dictionaries include words that can be classified as: positive, negative or neutral. The proposed system adopts machine learning classification technique to perform classification and prediction.

Polarity Classification of Arabic Sentiments

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic MSA, or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity DASAP. A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites i.e. Facebook, Blogs, YouTube, and Twitter. This dataset is used to evaluate the effectiveness of the proposed method DASAP. Receiver Operating Characteristic ROC prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Evaluating Web Accessibility Metrics for Jordanian Universities

University web portals are considered one of the main access gateways for universities. Typically, they have a large candidate audience among the current students, employees, and faculty members aside from previous and future students, employees, and faculty members. Web accessibility is the concept of providing web content universal access to different machines and people with different ages, skills, education levels, and abilities. Several web accessibility metrics have been proposed in previous years to measure web accessibility. We integrated and extracted common web accessibility metrics from the different accessibility tools used in this study. This study evaluates web accessibility metrics for 36 Jordanian universities and educational institute websites. We analyze the level of web accessibility using a number of available evaluation tools against the standard guidelines for web accessibility. Receiver operating characteristic quality measurements is used to evaluate the effectiveness of the integrated accessibility metrics.

HQTP: Holy Quran Transfer Protocol

With the increase of using Holy Quran network based applications, particularly smart phone devices, these applications could be a target of dangerous attacks targeting Islam. Therefore, Muslims community needs a standard networking rules for organizing a secure way for Holy Quran transmission. In this paper we propose a novel networking protocol, called HQTP (Holy Quran Transfer Protocol). The proposed HQTP presents an application layer protocol that aims to standardize common rules identifying a secure, reliable and effective way to transmit Holy Quran (for both text and audio) over the Internet. As any other protocol, HQTP will consist of a header and a payload, the header contains control data that guarantee reliability, integrity and availability. Payload contains Holy Quran data as a text or audio and other related information such as Quran translation and explanation. We discussed the security challenges and problems and we proposed techniques to solve them.