Authors:
Giacomo Frisoni
;
Gianluca Moro
and
Antonella Carbonaro
Affiliation:
Department of Computer Science and Engineering – DISI, University of Bologna, Via dell’Università 50, I-47522, Cesena, Italy
Keyword(s):
Text Mining, Descriptive Analytics, Explainability, Latent Semantic Analysis, Unsupervised Learning, Rare Diseases.
Abstract:
Though the strong evolution of knowledge learning models has characterized the last few years, the explanation of a phenomenon from text documents, called descriptive text mining, is still a difficult and poorly addressed problem. The need to work with unlabeled data, explainable approaches, unsupervised and domain independent solutions further increases the complexity of this task. Currently, existing techniques only partially solve the problem and have several limitations. In this paper, we propose a novel methodology of descriptive text mining, capable of offering accurate explanations in unsupervised settings and of quantifying the results based on their statistical significance. Considering the strong growth of patient communities on social platforms such as Facebook, we demonstrate the effectiveness of the contribution by taking the short social posts related to Esophageal Achalasia as a typical case study. Specifically, the methodology produces useful explanations about the ex
periences of patients and caregivers. Starting directly from the unlabeled patient’s posts, we derive correct scientific correlations among symptoms, drugs, treatments, foods and so on.
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