Evolving Text Data Stream Mining

TL;DR

This paper reviews challenges and proposes new models for mining evolving text data streams, addressing issues like high dimensionality, semantic representation, and label scarcity in real-time online social platform data.

Contribution

It introduces novel learning models for clustering and multi-label learning tailored to the unique properties of evolving text streams.

Findings

Improved clustering performance on high-dimensional text streams

Effective semantic representation capturing evolving topics

Models handle label scarcity in streaming data

Abstract

A text stream is an ordered sequence of text documents generated over time. A massive amount of such text data is generated by online social platforms every day. Designing an algorithm for such text streams to extract useful information is a challenging task due to unique properties of the stream such as infinite length, data sparsity, and evolution. Thereby, learning useful information from such streaming data under the constraint of limited time and memory has gained increasing attention. During the past decade, although many text stream mining algorithms have proposed, there still exists some potential issues. First, high-dimensional text data heavily degrades the learning performance until the model either works on subspace or reduces the global feature space. The second issue is to extract semantic text representation of documents and capture evolving topics over time. Moreover, the problem of label scarcity exists, whereas existing approaches work on the full availability of labeled data. To deal with these issues, in this thesis, new learning models are proposed for clustering and multi-label learning on text streams.