Latent Semantic Analysis for Privacy Preserving Peer Feedback

Selmi, Mouna; Hage, Hicham; Aïmeur, Esma

doi:10.1007/978-3-319-17127-2_7

Cited by 2 publications

(5 citation statements)

References 17 publications

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“…It is important to assess the role of weighting schemes since some weights have different effects depending on text size and nature. As this work is an extension of our previous work [5] in which we used LSA for privacy preserving, a weighting approach highlighting the non-frequent terms in a set of documents, particularly global schemes such as IDF will be unsuitable. Specifically, we assume that self-disclosing sentences are not frequent in peers' interactions.…”

Section: A Text Weighting Approachmentioning

confidence: 96%

“…For instance, [1] conducted automatic semantic analysis to support informal social learning by enabling visual exploration of the semantic spaces of usergenerated content. Alternatively, [3] used a probabilistic semantic model to analyse and visualize the collaborative writing process by extracting the semantic topics associated to its evolution, while [4] and [5] have used LSA to evaluate learners' contributions and compose peers' feedback respectively. LSA is one of these techniques used for analysing relationships between a set of documents and the terms they contain, by producing a set of latent concepts related to the documents and terms.…”

Section: A Lsa In Educational Contextmentioning

confidence: 99%

“…As a continuity of our previous work [5], we focus here on analysing the sensibility of LSA to evaluate the quality of peers' interactions and its ability to discard negative and self-disclosing feedback. LSA evaluation of peer feedback is closely associated to semantic relatedness which is the task of finding and quantifying the strength of the semantic connections that exist between textual units based on their representations in the semantic space.…”

Section: Introductionmentioning

confidence: 99%

“…In the literature, there are a number of semantic analysis techniques that have been used in learning environment to support collaborative activities [3], to evaluate learners' contributions [4], and to protect learners' privacy [5]. Regarding the latter work, a semantic representation of peers' textual interactions was built, using Latent Semantic Analysis (LSA), in order to discard negative and self-disclosing feedback by varying the semantic space parameters.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating LSA sensibility to disclosure in learners' interactions

Selmi

Hage

Aïmeur

2015

2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)

Self Cite

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Social technologies have been effectively applied in distant learning platforms to engage students in social interaction and active learning. Students' interaction is viewed as a process through which learners demonstrate their expertise and a transfer of assistance to their peers. Within this context, students may ask for peers' feedback when they encounter problems that they cannot solve themselves. In response to his request, a learner may receive a huge volume of peers' feedback which is not usually all positive, relevant and favourable to his learning. Discarding negative feedback (bullying, demeaning and those making an individual vulnerable by self-disclosing personal data) aligns to the first principle of e-learning environment which is to ensure for the learners to interact and collaborate free from fear in a safe e-learning environment. Scrutinizing the learners' natural language interactions requires the use of artificial intelligence techniques, namely Latent semantic Analysis (LSA). In this work, we analyse LSA's sensibility to the quality of peers' text-based interactions and its ability to discard negative and self-disclosing feedback. To do that, we built different LSA models by varying the approach parameters. We theorize that there would be positive correlations between LSA measures derived from the latent semantic space and human judges' rating. Regression analysis shows that once the LSA parameters that better represent the human judgments of feedback relevance and disclosure have been considered, LSA reliably predicts the human scores (r=.64, p<.001).

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Section: A Text Weighting Approachmentioning

confidence: 96%

Section: A Lsa In Educational Contextmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating LSA sensibility to disclosure in learners' interactions

Selmi

Hage

Aïmeur

2015

2015 10th International Conference on Intelligent Systems: Theories and Applications (SITA)

Self Cite

View full text Add to dashboard Cite

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“…Then today's e-learning systems have exposed the students' private information issues by using with the new option which comes with significantly increased risks to the privacy of the feedback requester, who reveal personal information based on Latent Semantic Analysis (LSA). [5] In this paper, we integrate the technologies on LSA and external knowledge libraries to propose a novel algorithm in the medical field and acquire sensitive terms relative to privacy policies automatically without training documents.…”

Section: Related Workmentioning

confidence: 99%

A Topic-Level Privacy Preserving Search in the Medical Field

Tian

Meng

et al. 2015

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity)

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With the explosive growth of medical information, the users not only query information efficiently and accurately, but also pay attention to the information of sensitivity and privacy. In medical domains, the current privacy preserving methods either use the technology of Access Control List, or need to prepare training documents for each privacy policy. However, it is a timeconsuming and impractical way for data owners to assign a privacy policy on each document. In this paper, by exploiting the privacy medical queries, we propose a novel approach based on semantic and ontology to achieve the topic-level privacy preserving search. With the support of them, we first mine all the potential hierarchy and semantics from a user query and acquire sensitive terms relative to privacy policies automatically without training documents.

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Latent Semantic Analysis for Privacy Preserving Peer Feedback

Cited by 2 publications

References 17 publications

Evaluating LSA sensibility to disclosure in learners' interactions

Evaluating LSA sensibility to disclosure in learners' interactions

A Topic-Level Privacy Preserving Search in the Medical Field

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