Text classification and sentiment analysis in social networks using a probability model
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Type of Workapplication/pdf
xiii, 104 pages
DepartmentTowson University. Department of Computer and Information Sciences
RightsCopyright protected, all rights reserved.
There are no restrictions on access to this document. An internet release form signed by the author to display this document online is on file with Towson University Special Collections and Archives.
In recent years, diverse social networks, such as Facebook, YouTube, and Twitter, have rapidly grown in size and influence, and a huge amount of data is being generated from the social networks in real time. Demands for data mining on social networks have been dramatically increasing, since analyzing the data can yield insights and understanding to real world phenomenon. However, there are a lot of challenges and difficulties with data collection, management, and analysis because of the features of the social networks' data: large, noisy, and dynamic. Therefore, this study will address the overall problems with data mining in social networks and improve existing data mining techniques. We propose an integrated data collection, management and analysis system. Furthermore, we propose specific analysis methods, such as topic classification, sentiment analysis, and seed selection algorithm to analyze social networks' data.