Unsupervised Anomaly Detection in Multi-Topic Short-Text Corpora

Unsupervised anomaly detection seeks to identify deviant data samples in a dataset without using labels and constitutes a challenging task, particularly when the majority class is heterogeneous. This paper addresses this topic for textual data and aims to determine whether a text sample is an outlier within a potentially multi-topic corpus. To this end, it is crucial to grasp the semantic aspects of words, particularly when dealing with short texts, since it is difficult to syntactically discriminate data samples based only on a few words. Thereby we make use of word embeddings to represent each sample by a dense vector, efficiently capturing the underlying semantics. Then, we rely on the Mixture Model approach to detect which samples deviate the most from the underlying distributions of the corpus. Experiments carried out on real datasets show the effectiveness of the proposed approach in comparison to state-ofthe-art techniques both in terms of performance and time efficiency, especially when more than one topic is present in the corpus.