Collaborative Filtering Recommendation of Educational Content in Social Environments Utilizing Sentiment Analysis Techniques

Karampiperis, Pythagoras; Koukourikos, Antonis; Stoitsis, Giannis

doi:10.1007/978-1-4939-0530-0_1

Cited by 20 publications

(15 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [RS73-2014] sentiment analysis techniques on user-generated comments of a repository of educational resources are used to obtain valuable qualitative information for adjusting the perceived rating of a given resource by a specific user [51]. …”

Section: Cluster 2: Improving Collaborative Filtering Algorithms Withmentioning

confidence: 99%

“…For this, user centered design approaches [87] can be of value, such as to consider recommending learning activities that, for instance, foster communication [1] and metacognition [124][77] [88]. At the same time, the potential of semantic technologies is being considered to describe the educational domain and therefore enrich the recommendation process In this sense, the application of affective computing in TEL recommender systems can provide added value to the recommendations when emotional and sentiment information is taken into account in the recommendation process [51] [92] and can provide interactive recommendations through sensorial actuators [91].…”

Section: Analysis According To the Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

195

115

View full text Add to dashboard Cite

Section: Cluster 2: Improving Collaborative Filtering Algorithms Withmentioning

confidence: 99%

Section: Analysis According To the Frameworkmentioning

confidence: 99%

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

195

115

View full text Add to dashboard Cite

“…Most experiments were executed on historical datasets, for example on MERLOT 14 [44], [46], [108], [146], or MACE 15 [54], [83], [85], [134] and Ariadne 16 [101]. Historical TEL datasets that fulfil all requirements for an evaluation are however hard to find [27].…”

Section: Offline Experimentsmentioning

confidence: 99%

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Erdt

Fernández

Rensing

2015

IEEE Trans. Learning Technol.

120

View full text Add to dashboard Cite

The increasing number of publications on recommender systems for Technology Enhanced Learning (TEL) evidence a growing interest in their development and deployment. In order to support learning, recommender systems for TEL need to consider specific requirements, which differ from the requirements for recommender systems in other domains like e-commerce. Consequently, these particular requirements motivate the incorporation of specific goals and methods in the evaluation process for TEL recommender systems. In this article, the diverse evaluation methods that have been applied to evaluate TEL recommender systems are investigated. A total of 235 articles are selected from major conferences, workshops, journals and books where relevant work have been published between 2000 and 2014. These articles are quantitatively analysed and classified according to the following criteria: type of evaluation methodology, subject of evaluation, and effects measured by the evaluation. Results from the survey suggest that there is a growing awareness in the research community of the necessity for more elaborate evaluations. At the same time, there is still substantial potential for further improvements. This survey highlights trends and discusses strengths and shortcomings of the evaluation of TEL recommender systems thus far, thereby aiming to stimulate researchers to contemplate novel evaluation approaches.

show abstract

“…The system uses supervised classification algorithms to filter spammers and promoters on the basis of attributes extracted from videos and user profiles. In another research [12] a user rating along with sentiment analysis technique have been applied by using a set of predetermined polarity terms to recommend educational content in social environments. The technique makes the result more accurate as the text is analyzed and polarity is also judged through sentiments before recommending the content to another user.…”

Section: Recommender System Based Filtering Techniquesmentioning

confidence: 99%

Text-Based Intelligent Content Filtering on Social Platforms

Khurshid

Khan

Bashir

2014

2014 12th International Conference on Frontiers of Information Technology

View full text Add to dashboard Cite

Social platforms have become one of the popular mediums of information sharing and communication over the Internet today. People share all types of contents such as text, images, audio and video using these social platforms. Though information gained using these social platforms can be very useful for people around the globe, some of the user generated contents are very negative as they contain abusive, racial, offensive and insulting material. Thus, there is a need for an effective online content filtering technique which blocks these negative contents while not disturbing the access of users to rest of the contents available on these sites. Current techniques simply filter on the basis of URLs blocking and keyword matching or either rely on a large database of pre-classified web addresses. The problem is how to intelligently filter the negative contents, rather than filtering entire websites using their URLs or applying simple keyword matching techniques. In this paper we review a number of existing approaches to content filtering and propose an intelligent content filtering technique that uses sentiment analysis of the text and feature engineering methods to perform text classification.

show abstract

Collaborative Filtering Recommendation of Educational Content in Social Environments Utilizing Sentiment Analysis Techniques

Cited by 20 publications

References 19 publications

Panorama of Recommender Systems to Support Learning

Panorama of Recommender Systems to Support Learning

Evaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey

Text-Based Intelligent Content Filtering on Social Platforms

Contact Info

Product

Resources

About