Text Stream Classification Techniques and Research Issues: A Review

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Abhinandan Vishwakarma
Prof. Sini Shibu


With the rapid growth of applications that generates massive text streams, text stream classification is the key technique for handling and organizing text data. Text classification is the process of sorting documents containing text streams into one or more predefined categories. These streams are generated continuously and in a very high fluctuating data rates, example includes social networks, news collections, chat and e-mail etc. These streams are transient open end rather than persistent on disk. As text streams are dynamic, infinite in length, can’t reproduce for processing, arrives at very high speed, text stream classification is more challenging as compare to static text classification. However most of the reported work are concentrated on structural data and seldom focus on unstructured data such as textual document. In this paper we present a foundation on text stream analysis, text stream classification, challenges in classification and identify the direction of future research.

Keywords: Text stream, data stream, text stream classification, data stream classification, pre-processing.


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