Short Text Classification on Complaint Documents

Indonesian government has developed a system for citizens to voice their aspirations and complaints, which are then stored in the form of short documents. Unfortunately, the existing system employs human annotators to manually categorize the short documents, which is very expensive and time-consuming. As a result, automatically classifying the short documents into their correct topics will reduce manual works and obviously increase the efficiency of the task itself. In this paper, we propose several approaches to automatically classify these short documents using various features, such as unigrams, bigrams, and their combination. Moreover, we also demonstrate the use of information gain and Latent Dirichlet Allocation (LDA) for selecting discriminative features.