English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments
Online teaching and learning has been beneficial in facilitating learning of English as a foreign language (EFL). In online EFL learning, YouTube is one of the most utilized information and communication technology (ICT) tools because of its inherent features that make it a unique environment for le...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/2/24 |
| _version_ | 1797621658678198272 |
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| author | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf |
| author_facet | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf |
| author_sort | Husam M. Alawadh |
| collection | DOAJ |
| description | Online teaching and learning has been beneficial in facilitating learning of English as a foreign language (EFL). In online EFL learning, YouTube is one of the most utilized information and communication technology (ICT) tools because of its inherent features that make it a unique environment for learners and educators. Many interesting aspects of YouTube-based learning can be beneficial in supplementing conventional classroom methods, and, therefore, such aspects must be identified. Previous scholarly work aimed at improving YouTube learning environment was predominantly conducted manually by gathering learners’ impressions through interviews and questionnaires to analyze the differences between YouTube- and classroom-based EFL learning. However, such methods are tedious and time-consuming and can lead to results that are of less generalizable implications. User comments on YouTube channels are useful in identifying such aspects, as they present a wealth of information related to the quality of the content provided, challenges the targeted audience faces, and areas of potential improvement. Therefore, in our current study, YouTube API is used to collect the comments of three randomly selected and popular YouTube channels. Following a data cleaning process, people’s sentiments about EFL learning were first identified via a TextBlob method. Second, the automated latent semantic analysis (LSA) method of topic finding was used to collect global and open-ended topics of discussion on YouTube-based EFL learning. Users’ sentiments on the most popular topics of discussion are discussed in this paper. Further, based on the results, hypothetical findings on YouTube EFL learning are provided as recommendation for future content, including more variety of the content covered, introduction of the meanings and punctuation following words, the design of the course such that it addresses a multinational audience of any age, and targeted teaching of each variety of English, such as British and American. We also make suggestions for learners of English who wish to utilize online and offline learning, which include finding the course of interest first based on one’s needs which can be discussed with a tutor or any English teacher to optimize the learning experience, participating in fearless educator–learner interaction and engagement, and asking other EFL learners for their previous experiences with learning online in order for the learner to maximize benefit. |
| first_indexed | 2024-03-11T08:59:04Z |
| format | Article |
| id | doaj.art-81f9e275ecf74d2eb157bb582f29a538 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T08:59:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-81f9e275ecf74d2eb157bb582f29a5382023-11-16T19:53:07ZengMDPI AGComputers2073-431X2023-01-011222410.3390/computers12020024English Language Learning via YouTube: An NLP-Based Analysis of Users’ CommentsHusam M. Alawadh0Amerah Alabrah1Talha Meraj2Hafiz Tayyab Rauf3Department of English Language and Translation, College of Languages and Translation, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt 47040, PakistanCentre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UKOnline teaching and learning has been beneficial in facilitating learning of English as a foreign language (EFL). In online EFL learning, YouTube is one of the most utilized information and communication technology (ICT) tools because of its inherent features that make it a unique environment for learners and educators. Many interesting aspects of YouTube-based learning can be beneficial in supplementing conventional classroom methods, and, therefore, such aspects must be identified. Previous scholarly work aimed at improving YouTube learning environment was predominantly conducted manually by gathering learners’ impressions through interviews and questionnaires to analyze the differences between YouTube- and classroom-based EFL learning. However, such methods are tedious and time-consuming and can lead to results that are of less generalizable implications. User comments on YouTube channels are useful in identifying such aspects, as they present a wealth of information related to the quality of the content provided, challenges the targeted audience faces, and areas of potential improvement. Therefore, in our current study, YouTube API is used to collect the comments of three randomly selected and popular YouTube channels. Following a data cleaning process, people’s sentiments about EFL learning were first identified via a TextBlob method. Second, the automated latent semantic analysis (LSA) method of topic finding was used to collect global and open-ended topics of discussion on YouTube-based EFL learning. Users’ sentiments on the most popular topics of discussion are discussed in this paper. Further, based on the results, hypothetical findings on YouTube EFL learning are provided as recommendation for future content, including more variety of the content covered, introduction of the meanings and punctuation following words, the design of the course such that it addresses a multinational audience of any age, and targeted teaching of each variety of English, such as British and American. We also make suggestions for learners of English who wish to utilize online and offline learning, which include finding the course of interest first based on one’s needs which can be discussed with a tutor or any English teacher to optimize the learning experience, participating in fearless educator–learner interaction and engagement, and asking other EFL learners for their previous experiences with learning online in order for the learner to maximize benefit.https://www.mdpi.com/2073-431X/12/2/24English learningonline educationsentiment analysistopic modelingYouTube learning |
| spellingShingle | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments Computers English learning online education sentiment analysis topic modeling YouTube learning |
| title | English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments |
| title_full | English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments |
| title_fullStr | English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments |
| title_full_unstemmed | English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments |
| title_short | English Language Learning via YouTube: An NLP-Based Analysis of Users’ Comments |
| title_sort | english language learning via youtube an nlp based analysis of users comments |
| topic | English learning online education sentiment analysis topic modeling YouTube learning |
| url | https://www.mdpi.com/2073-431X/12/2/24 |
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