DocumentCode
251948
Title
The Evaluation of the Public Opinion - A Case Study: MERS-CoV Infection Virus in KSA
Author
Zarrad, Anis ; Jaloud, Abdulaziz ; Alsmadi, Izzat
Author_Institution
Dept. of Comput. Sci. & Inf. Syst., Prince Sultan Univ., Riyadh, Saudi Arabia
fYear
2014
fDate
8-11 Dec. 2014
Firstpage
664
Lastpage
670
Abstract
Opinion Mining and Sentiment Analysis are active research trends in natural language processing and data mining. Recently, this research has been extended outside the computer science area to cover other areas such as social science, political science, and business. The explosion of social media such as social networks, Blogs, Twitter, and forums has created unprecedented opportunities for data mining research community. Analyzers can study and analyze users´ opinions, attitudes, and emotions about news or social events. Big data focuses on the intelligent analysis of a large amount of data that is typically collected from several different sources. Our focus in this work is to address new challenges raised by combining Apache Hadoop as a big data platform with an opinion mining approach to make a decision we often seek based on collected data from the opinions of people. We presented a case study about MERS virus in KSA to evaluate our proposed approach. A discussion of available dataset and results are also provided.
Keywords
Big Data; data mining; natural language processing; social networking (online); Apache Hadoop; Big Data; Blogs; KSA; MERS-CoV infection virus; Twitter; data mining research community; intelligent analysis; natural language processing; opinion mining; public opinion; sentiment analysis; social media; social networks; Big data; Classification algorithms; Computer science; Data mining; Sentiment analysis; Twitter; Big Data; Hadoop; MERS-CoV infection Virus; Opinion mining; Social Networks; sentimental analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Utility and Cloud Computing (UCC), 2014 IEEE/ACM 7th International Conference on
Conference_Location
London
Type
conf
DOI
10.1109/UCC.2014.107
Filename
7027574
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