We developed a system that will acquire Arabic text from Twitter and extract users' opinions towards different topics and products. In the Data Acquisition phase, we collected tweets from Twitter related to specific topics.
In the Tweet-Filtering phase, we reduced the noise in the collected tweets data to facilitate the Annotation phase, in which we annotated the collected tweets depending on the specified topic.
Hence, in this thesis, we studied sentiment analysis of Arabic text retrieved from a well-known social media site, namely Twitter.
Specifically, we studied the topic of target-dependent sentiment analysis of Arabic Twitter text, which has not been addressed in Arabic language before.
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Now-a-days many of web users post their opinions for many products through blogs, review sites and social networking sites.
Business organizations and corporate organizations are always eager to find consumer or individual views regarding their products, support and service.
Available are both soft and hard copies of the thesis.
The opinions posted range from criticizing politicians to discussing football matches, citing top news, appraising movies, and recommending new products and services such as mobiles, restaurants, and software.