SAFE: A Sentiment Analysis Framework for E-Learning

Abstract

The spread of social networks allows sharing opinions on different aspects of life and daily millions of messages appear on the web. This textual information can be a rich source of data for opinion mining and sentiment analysis: the computational study of opinions, sentiments and emotions expressed in a text. Its main aim is the identification of the agreement or disagreement statements that deal with positive or negative feelings in comments or reviews. In this paper, we investigate the adoption, in the field of the e-learning, of a probabilistic approach based on the Latent Dirichlet Allocation (LDA) as Sentiment grabber. By this approach, for a set of documents belonging to a same knowledge domain, a graph, the Mixed Graph of Terms, can be automatically extracted. The paper shows how this graph contains a set of weighted word pairs, which are discriminative for sentiment classification. In this way, the system can detect the feeling of students on some topics and teacher can better tune his/her teaching approach. In fact, the proposed method has been tested on datasets coming from e-learning platforms. A preliminary experimental campaign shows how the proposed approach is effective and satisfactory.

Evaluation of an international medical E-learning course with natural language processing and machine learning

“…Machine learning methods have been implemented in other areas of education research [ 51 ] and are used commercially in search engines and social media [ 20 , 52 ]. Data mining has been used in educational studies generally (not medical education) to gather overall sentiment [ 16 – 18 ] and topic modelling [ 19 ] as done in the present study, but also for ‘student modelling’ whereby students’ predicted preferences for teaching and course outcomes are modelled [ 53 – 55 ]. In non-medical MOOCs, recent literature has emerged on the use of topic modelling and sentiment analyses on monitoring feedback and discussions [ 56 – 58 ].…”

“…These methods have been used in educational studies generally to gather overall sentiment [ 16 – 18 ] of bodies of free text and to generate overarching topics or themes from this [ 19 ]. However, these methods have yet to be utilised in medical education contexts [ 20 , 21 ].…”

Evaluation of an International Medical E-Learning Course with Natural Language Processing and Machine Learning

“…Machine learning methods have been implemented in other areas of education research [47] and are used commercially in search engines and social media [16,48]. Data mining has been used in educational studies generally (not medical education) to gather overall sentiment [49][50][51] and topic modelling [52] as done in the present study, but also for 'student modelling' whereby students' predicted preferences for teaching and course outcomes are modelled [53][54][55]. In non-medical MOOCs, recent literature has emerged on the use of topic modelling and sentiment analyses on monitoring feedback and discussions [56][57][58].…”

Making Sense of Qualitative Open-ended Reviews

“…al. [11] used a probabilistic approach based on the Latent Drichlet Allocation to understand sentiments from textual data in an e-learning environment.…”