Opinion Mining and Analysis for Arabic Language
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2014, Vol 5, Issue 5
Abstract
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 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 In addition 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 In addition subjective. In addition, the huge amount of data contained in social networks can make it impractical to perform analysis manually. This paper focuses on evaluating social content in Arabic language and contexts. 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 or opinions as input and generates polarity based outputs related to the comments. For example the output can be whether 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.
Authors and Affiliations
Mohammed Al-Kabi, Izzat Alsmadi, Amal Gigieh, Heider Wahsheh, Mohamad Haidar
Keywords
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- EP ID EP115976
- DOI 10.14569/IJACSA.2014.050528
- Views 104
- Downloads 0
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