Named Entity Recognition Using Stanford Classes and NLTK

Interests play an essential role in the process of learning, thereby enriching learners ‘interests will yield to an enhanced experience in MOOCs. Learners interact freely and spontaneously on social media through different forms of user-generated content which contain hidden information that reveals their real interests and preferences. In this paper, we aim to identify and extract the topical interest from the text content shared by learners on social media to enrich their course preferences in MOOCs. We apply NLP pipeline and topic modeling techniques to the textual feature using three well-known topic models: Latent Dirichlet Allocation, Latent Semantic Analysis, and BERTopic. The results of our experimentation have shown that BERTopic performed better on the scrapped dataset.

show abstract

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

Zankadi

Idrissi

Daoudi

et al. 2022

Educ Inf Technol

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Named Entity Recognition Using Stanford Classes and NLTK

Cited by 1 publication

References 14 publications

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

Identifying learners’ topical interests from social media content to enrich their course preferences in MOOCs using topic modeling and NLP techniques

Contact Info

Product

Resources

About