Text Stream Classification: Literature Review and Current Trends

Abstract

Text stream classification is the task of assigning labels to a sequence of text data as it arrives in real time. This is a challenging task because the data is often incomplete and noisy, and the labels may change over time. In this paper, we review the state-of-the-art in text stream classification models. We discuss the different types of models that have been proposed, as well as the strengths and weaknesses of each approach. We also identify some of the open challenges in text stream classification, and suggest directions for future research.