Sports-fanaticism formalism for sentiment analysis in Arabic text

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mohammed Alqmase ◽  
Husni Al-Muhtaseb ◽  
Habib Rabaan
Author(s):  
Alaa Abdalqahar Jihad ◽  
Ahmed Subhi Abdalkafor

<p>Over the last decade there has been an increase in number of E-mails or comments to a company via social media sites, to satisfy their customers, the company must take in to consideration these messages and comments and know whether the customers are satisfied with what the company offers or not. Several techniques have been proposed to analyze the sentiment of the comment writer. Dealing with the Arabic language is faced with many challenges, such as it is a morphologically rich language and how to return the word to its original root. In this paper the challenges of dealing with the Arabic language were reviewed and a framework was also established to analyze the comments in Arabic and classify it into positive, negative or neutral sentiment. The framework was trained and tested and then the con-clusions were drawn based on its work.</p>


2017 ◽  
Vol 117 ◽  
pp. 129-136 ◽  
Author(s):  
Maher Itani ◽  
Chris Roast ◽  
Samir Al-Khayatt

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Afnan S. Al-Subaihin ◽  
Hend S. Al-Khalifa

We present the implementation and evaluation of a sentiment analysis system that is conducted over Arabic text with evaluative content. Our system is broken into two different components. The first component is a game that enables users to annotate large corpuses of text in a fun manner. The game produces necessary linguistic resources that will be used by the second component which is the sentimental analyzer. Two different algorithms have been designed to employ these linguistic resources to analyze text and classify it according to its sentimental polarity. The first approach is using sentimental tag patterns, which reached a precision level of 56.14%. The second approach is the sentimental majority approach which relies on calculating the number of negative and positive phrases in the sentence and classifying the sentence according to the dominant polarity. The results after evaluating the system for the first sentimental majority approach yielded the highest accuracy level reached by our system which is 60.5% while the second variation scored an accuracy of 60.32%.


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