Concept Drift Adaptation in Text Stream Mining Settings: A Systematic Review

TL;DR

This study presents a systematic literature review regarding concept drift adaptation in text stream scenarios, selecting 48 papers published between 2018 and August 2024 to unravel aspects such as text drift categories, detection types, model update mechanisms, stream mining tasks addressed, and text representation methods and their update mechanisms.

Abstract

The society produces textual data online in several ways, e.g., via reviews and social media posts. Therefore, numerous researchers have been working on discovering patterns in textual data that can indicate peoples' opinions, interests, etc. Most tasks regarding natural language processing are addressed using traditional machine learning methods and static datasets. This setting can lead to several problems, e.g., outdated datasets and models, which degrade in performance over time. This is particularly true regarding concept drift, in which the data distribution changes over time. Furthermore, text streaming scenarios also exhibit further challenges, such as the high speed at which data arrives over time. Models for stream scenarios must adhere to the aforementioned constraints while learning from the stream, thus storing texts for limited periods and consuming low memory. This study presents a systematic literature review regarding concept drift adaptation in text stream scenarios. Considering well-defined criteria, we selected 48 papers published between 2018 and August 2024 to unravel aspects such as text drift categories, detection types, model update mechanisms, stream mining tasks addressed, and text representation methods and their update mechanisms. Furthermore, we discussed drift visualization and simulation and listed real-world datasets used in the selected papers. Finally, we brought forward a discussion on existing works in the area, also highlighting open challenges and future research directions for the community.

Figures (12)

• Figure 1: Intersection of subjects of interest in this review. We are mainly interested in the papers on the intersection (hatched area) of these three subjects.
• Figure 2: Types of concept drift. Adapted from gama2014survey. Each marker, i.e., circle, and diamond, represents an arbitrary class/label. Dashed lines correspond to the border between regions of classes.
• Figure 3: Dynamics of concept drift over time. Adapted from gama2014survey.
• Figure 4: Semantic shift across several decades or centuries. Adapted from hamilton2016diachronic.
• Figure 5: Process of papers selection. Each rounded-corner rectangle on the right side corresponds to an exclusion criterion. The numbers of remaining studies after each elimination are presented on the left side.