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Communication Dans Un Congrès Année : 2023

Machine Learning for Text Anomaly Detection: A Systematic Review

Résumé

Anomaly detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing reviews mainly focus on structured data, such as numerical or categorical data. Several studies treated review of anomaly detection in general on heterogeneous data or concerning a specific domain. However, anomaly detection on unstructured textual data is less treated. In this work, we target textual anomaly detection. Thus, we propose a systematic review of anomaly detection solutions in the text. To do so, we analyze the included papers in our survey in terms of anomaly detection types, feature extraction methods, and machine learning methods. We also introduce a web scrapping to collect papers from digital libraries and propose a clustering method to classify selected papers automatically. Finally, we compare the proposed automatic clustering approach with manual classification, and we show the interest of our contribution.
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Dates et versions

hal-04130252 , version 1 (15-06-2023)

Identifiants

  • HAL Id : hal-04130252 , version 1

Citer

Karima Boutalbi, Faiza Loukil, David Telisson, Kavé Salamatian, Hervé Verjus. Machine Learning for Text Anomaly Detection: A Systematic Review. The 5th IEEE International Workshop on Deep Analysis of Data-Driven Applications, Jun 2023, Turin, Italy. ⟨hal-04130252⟩

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