  Due to the advent and increase in the popularity of the Internet, people have
been producing and disseminating textual data in several ways, such as reviews,
social media posts, and news articles. As a result, numerous researchers have
been working on discovering patterns in textual data, especially because social
media posts function as social sensors, indicating peoples' opinions,
interests, etc. However, most tasks regarding natural language processing are
addressed using traditional machine learning methods and static datasets. This
setting can lead to several problems, such as an outdated dataset, which may
not correspond to reality, and an outdated model, which has its performance
degrading over time. Concept drift is another aspect that emphasizes these
issues, which corresponds to data distribution and pattern changes. In a text
stream scenario, it is even more challenging due to its characteristics, such
as the high speed and data arriving sequentially. In addition, models for this
type of scenario must adhere to the constraints mentioned above while learning
from the stream by storing texts for a limited time and consuming low memory.
In this study, we performed a systematic literature review regarding concept
drift adaptation in text stream scenarios. Considering well-defined criteria,
we selected 40 papers to unravel aspects such as text drift categories, types
of text drift detection, model update mechanism, the addressed stream mining
tasks, types of text representations, and text representation update mechanism.
In addition, we discussed drift visualization and simulation and listed
real-world datasets used in the selected papers. Therefore, this paper
comprehensively reviews the concept drift adaptation in text stream mining
scenarios.
