No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling

• URL: http://arxiv.org/abs/2208.01712v1
• Date: Tue, 2 Aug 2022 19:51:43 GMT
• Title: No Pattern, No Recognition: a Survey about Reproducibility and Distortion Issues of Text Clustering and Topic Modeling
• Authors: Mar\'ilia Costa Rosendo Silva, Felipe Alves Siqueira, Jo\~ao Pedro Mantovani Tarrega, Jo\~ao Vitor Pataca Beinotti, Augusto Sousa Nunes, Miguel de Mattos Gardini, Vin\'icius Adolfo Pereira da Silva, N\'adia F\'elix Felipe da Silva, Andr\'e Carlos Ponce de Leon Ferreira de Carvalho
• Abstract summary: Machine learning algorithms can be used to extract knowledge from unlabeled texts. Unsupervised learning can lead to variability depending on the machine learning algorithm. The presence of outliers and anomalies can be a determining factor.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extracting knowledge from unlabeled texts using machine learning algorithms can be complex. Document categorization and information retrieval are two applications that may benefit from unsupervised learning (e.g., text clustering and topic modeling), including exploratory data analysis. However, the unsupervised learning paradigm poses reproducibility issues. The initialization can lead to variability depending on the machine learning algorithm. Furthermore, the distortions can be misleading when regarding cluster geometry. Amongst the causes, the presence of outliers and anomalies can be a determining factor. Despite the relevance of initialization and outlier issues for text clustering and topic modeling, the authors did not find an in-depth analysis of them. This survey provides a systematic literature review (2011-2022) of these subareas and proposes a common terminology since similar procedures have different terms. The authors describe research opportunities, trends, and open issues. The appendices summarize the theoretical background of the text vectorization, the factorization, and the clustering algorithms that are directly or indirectly related to the reviewed works.

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