運用短文件分類技術改良微網誌政府服務之研究

Abstract

Web2.0的興起提供了政府以及市民們一條直接溝通的管道。近年來此項技術中，微網誌服務如推特(Twitter)已經成為了當今最熱門的應用之一。許多政府服務也在一些議題中利用微網誌從社會大眾蒐集各式各樣的見解。一般來說，市民們對於可以利用這類媒體來訴求他們的意見感到滿意進而提升人民對政府的信任程度。然而，隨著近年來微網誌的數量呈現指數型成長，因此勢必需要運用文字探勘相關技術來有效率的分析這些大量短文章。在這篇研究中，我們對於微網誌政府服務提出了一個有效率的分類架構。為了去解決微網誌的資料稀疏性問題，運用外部知識庫以及微網誌本身提供的時間資訊來修改原始貝式分類模型中的事前以及條件機率。利用311NYC 資料集的實驗顯示出我們所提出的分類架構可以準確的分類市民對於政府服務的見解，並且對貝式分類模型有顯著的改善。

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.