Vietnamese Short Text Classification via Distributed Computation

Social networking has been growing rapidly in Vietnam. The sharing information is diverse and circulates in many forms. It requires user-friendly solutions such as topic sorting and perspectives analysis in analyzing community trends, advertisements or anticipating and monitoring the spread of bad news. Unfortunately, Vietnamese is highly different from other languages and little research has been conducted in the literature on messages classification. The implementation of machine learning models on Vietnamese has not been thoroughly investigated and these models’ performance is unknown when applying in a different language. Vietnamese text is a serialization of syllables, hence, word boundary identification is not trivial. This research portrays our endeavor to construct an effective distributed framework for addressing the task of classification of short Vietnamese texts on social networks using the idea of probability categorization. The authors argue that addressing the task sharps the successful combination of machine learning, nat-ural language processing, and ambient intelligence. The proposed framework is effective and enables fast calculation, suitable for implementation in Apache Spark, meeting the demand for dealing with large amounts of textual data on the current social networks. Our data has been collected from several online text sources of 12412 short messages classified into 5 different topics. The evaluation shows that our approach has achieved an average of 82.73% classification accuracy. Thoughtfully learning the literature, we could state that this is the first attempt to classify short Vietnamese messages under a distributed computation framework.