Sentiment Analysis for Arabic Social Media | ||||
| IJCI. International Journal of Computers and Information | ||||
| Article 2, Volume 7, Issue 1, 2020, Page 14-31 PDF (776.65 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijci.2020.16170.1004 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
manal zayed  1; hamdi mousa2; Mohamed Elmenshawy3
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| 1the Faculty of Computers and Information, Department of Computer Science, Menoufia University | ||||
| 2faculty of computers and information | ||||
| 3Faculty of Computers and Information | ||||
| Abstract | ||||
| With the spread of social media services in Arabic societies, it leads to the explosive growth of Arabic posts, or comments. These services generate a huge volume of opinionated data on different topics such as politics and businesses. Analyzing valuable subjective information from data would assist in a better understanding and making decisions. Therefore, sentiment analysis coincides with social media networks and has become the most interesting research field in the sentiment analysis process. However, there are several challenges faced the sentiment analysis process. Arabic Sentiment analysis is indeed in its infantile stage and it has not obtained thoroughly attention wherein several challenges still need to address. Some of these challenges result from the complexity of Arabic natural language and other challenges result from social media platform itself. In this manuscript, we first study the impact of social media challenges on the challenges of Arabic language. Our findings show that such challenges add more complexities to the sentiment analysis process. Based on these findings, we review the contributed proposals, which give rise on analyzing Arabic social media data. Our review methodology is based on a set of criteria, which we propose to assess the advantages and limitations of these proposals. The interesting point here is to help researchers identify the social sentiment analysis problems along with a comprehensive survey on the sentiment analysis levels and classification approaches. Finally, we compare these proposals in terms of the average accuracy and suggest a new hybrid approach based on our findings. | ||||
| Keywords | ||||
| Arabic social media; Aspect level; Sentiment analysis; Twitter sentiment analysis | ||||
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