| Summary: | 碩士 === 國立臺灣大學 === 資訊管理學研究所 === 100 === The prevalence of Web2.0 techniques enables governments and citizens to communicate in a direct manner. Among the current Web2.0 applications, micro-blogging services, such as Twitter, are the most popular and many government services now exploit micro-blogs to collect opinions from the public on a range of issues. Generally, citizens are satisfied with this medium for expressing their opinions; however, as the number of micro-blogs is increasing exponentially, text mining is needed to analyze the opinions efficiently. In this paper, we propose an efficient classification framework for micro blog-based government services. To address the text sparseness problem of micro-blogs, an external knowledge base and the temporal information of micro blogs are used to modify the prior and conditional probabilities of the Naive Bayes classification model. Experiments based on the 311NYC dataset show that the proposed framework classifies citizens’ opinions about government services correctly, and it achieves a significant improvement over the Naive Bayes model.
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