A Comparative Study of Text Mining Algorithms for Anomaly Detection in Online Social Networks

Kokatnoor, Sujatha Arun; Balachandran, K.

doi:10.1007/978-981-15-5309-7_4

Cited by 1 publication

(1 citation statement)

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“…Several literature reviews have addressed AD in text data but each addressed a specific domain or application. Kokatnoor et al [11] provided a comparative study of text mining algorithms for AD in online social networks, but is restricted to a comparative analysis of the performance of four classification algorithms for a Twitter dataset. Finally, Mangathayaru et al [14] explored a text mining-based approach for intrusion detection.…”

Section: Related Literature Reviewsmentioning

confidence: 99%

Machine Learning for Text Anomaly Detection: A Systematic Review

Boutalbi

Loukil

Verjus

et al. 2023

2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)

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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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Section: Related Literature Reviewsmentioning

confidence: 99%