  In this work, a robust autoencoder ensemble-based approach designed to
address anomaly detection in text corpora is introduced. Each autoencoder
within the ensemble incorporates a local robust subspace recovery projection of
the original data in its encoding embedding, leveraging the geometric
properties of the k-nearest neighbors to optimize subspace recovery and
identify anomalous patterns in textual data. The evaluation of such an approach
needs an experimental setting dedicated to the context of textual anomaly
detection. Thus, beforehand, a comprehensive real-world taxonomy is introduced
to distinguish between independent anomalies and contextual anomalies. Such a
study to identify clearly the kinds of anomalies appearing in a textual context
aims at addressing a critical gap in the existing literature. Then, extensive
experiments on classical text corpora have been conducted and their results are
presented that highlights the efficiency, both in robustness and in
performance, of the robust autoencoder ensemble-based approach when detecting
both independent and contextual anomalies. Diverse range of tasks, including
classification, sentiment analysis, and spam detection, across eight different
corpora, have been studied in these experiments.
