[Demo] Integration of Text- and Web-Mining Results in EpidVis

Fadloun, Samilha; Sallaberry, Arnaud; Mercier, Alizé; Arsevska, Elena; Poncelet, Pascal; Roche, Mathieu

doi:10.1007/978-3-319-91947-8_45

Cited by 1 publication

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it can also be integrated with other domains such as machine learning, given its advancements in various fields including medical research [3][4][5], agriculture [6,7], data science [5,8], and more. From machine learning, adding a text analysis and natural language processing [8,9] would be interesting, especially in relation to software policies, to extract transparency requirements, keywords, and relationships.…”

Section: Introductionmentioning

confidence: 99%

“…Visual analytics [10,11,17] are widely used in both theory and practice. We may also find other fields that have used information visualization and data analysis in visual analytics, like mathematics [18], machine learning (https://trustmlvis.lnu.se/ (accessed on 15 May 2023)) [19,20], and natural language processing (NLP) [9,21] (as a subfield of machine learning). Beginning in 2010, the number of papers proposing visual analytics with NLP [22] has grown significantly.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Visual Analytics Using Machine Learning for Transparency Requirements

Fadloun,

Bennamane,

Meshoul

et al. 2023

Mathematics

View full text Add to dashboard Cite

Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often lengthy and complex, making it challenging for users to fully grasp their content. Additionally, existing transparency analytics tools typically rely on the manual extraction of information elements, resulting in a time-consuming process. To address these challenges, this paper proposes a novel approach that combines information visualization and machine learning analyses to automate the retrieval of information elements. The methodology involves the creation and labeling of a dataset derived from multiple software terms of use. Machine learning models, including naïve Bayes, BART, and LSTM, are utilized for the classification of information elements and text summarization. Furthermore, the proposed approach is integrated into our existing visualization tool TranspVis to enable the automatic detection and display of software information elements. The system is thoroughly evaluated using a database-connected tool, incorporating various metrics and expert opinions. The results of our study demonstrate the promising potential of our approach, serving as an initial step in this field. Our solution not only addresses the challenge of extracting information elements from complex terms of service but also provides a foundation for future research in this area.

show abstract