  Text classification is the most fundamental and essential task in natural
language processing. The last decade has seen a surge of research in this area
due to the unprecedented success of deep learning. Numerous methods, datasets,
and evaluation metrics have been proposed in the literature, raising the need
for a comprehensive and updated survey. This paper fills the gap by reviewing
the state-of-the-art approaches from 1961 to 2021, focusing on models from
traditional models to deep learning. We create a taxonomy for text
classification according to the text involved and the models used for feature
extraction and classification. We then discuss each of these categories in
detail, dealing with both the technical developments and benchmark datasets
that support tests of predictions. A comprehensive comparison between different
techniques, as well as identifying the pros and cons of various evaluation
metrics are also provided in this survey. Finally, we conclude by summarizing
key implications, future research directions, and the challenges facing the
research area.
