An increasing number of government services are exploiting Web2.0 applications to obtain instant feedback from the public. Generally, micro-blogs (e.g., Twitter) can provide seamless communications between governments and citizens. However, the problems of information overload and text sparseness make micro-blog management a difficult task. To solve the problems, we have proposed a classification framework that incorporates an external knowledge base and the temporal information of tweets into the Naive Bayes classification model. We further employ two smoothing techniques to leverage the temporal distribution, consider user content to enrich the content of the training tweets, and incorporate the WordNet synonyms into our classification model. Experiments based on the 311NYC dataset demonstrate that the proposed framework classifies tweets correctly and achieves a significant improvement over the Naive Bayes model.
This paper focuses on the publication time of tweets. In a future work, we will investigate different metadata of tweets to enhance the proposed framework. For instance, the social network of Twitter users could be investigated to identify opinion leaders for a certain service class. Analysing the tweets published by opinion leaders would help governments identify the subjects that citizens regard as the most important. We believe that by incorporating text mining techniques into Government2.0, government services will become more transformative, available, and interactive.
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