Traditional methods of instruction have undergone significant transformation due to the emerging technology and internet, promoting interactive and collaborative informal learning communities in digital platforms. In the digital landscape of this era, educational and technological innovation has fundamentally changed how learners engage with educational materials, which are the key affordances and challenges of Education 4.0 and Education 5.0 (Facer & Daanen, 2007; Santos et al., 2024). They emerged as an essential component in enhancing platforms such as Internet of Things (IoT), YouTube, social media, metaverse, and over-the-top (OTT) media services employed as learning experiences (Barry et al., 2016; Bonfield et al., 2020; Chopra, 2025; Duffy, 2008; García-Peñalvo, 2018; Im & Park, 2025; Lee & Hwang, 2023; Moghavvemi et al., 2018; Rho, 2024). These have become invaluable tools in contemporary education, providing learners with numerous opportunities to engage with content and promoting deeper involvement through multimedia elements.

Previous studies on English language teaching and learning have predominantly focused on formal educational settings, emphasizing classroom-based instruction, curriculum development, and teacher-led methodologies (Ellis, 2008; Richards & Rodgers, 2014). While research on informal language learning has gained traction in recent years, much of it has centered on mobile-assisted language learning and self-directed study through digital resources (Benson, 2011, 2017; Sockett, 2014). The role of online communities of practice, particularly on social media and video-sharing platforms like YouTube, has become popular and may have benefits in English education. These platforms facilitate peer-to-peer learning, collaborative knowledge construction, and engagement with authentic linguistic input in ways that diverge from traditional pedagogical approaches (Shoufan & Mohamed, 2022; Wang & Chen, 2020). While studies acknowledge the authenticity and accessibility of YouTube content (Shoufan & Mohamed, 2022), few have investigated how its comment sections can be harnessed as pedagogical tools to foster active engagement and corrective feedback, particularly for pronunciation challenges like the English diphthong [ou] in “focus.” Millennials and Generation Z increasingly utilize YouTube comment sections as digital forums for democratic discourse, fostering critical thinking and intellectual engagement (Schmidt et al., 2017; Statista, 2025; Zimmermann et al., 2022). These interactive virtual communities of practice (VCoP) enable users to construct and exchange knowledge, articulate diverse perspectives, and refine their cognitive and linguistic proficiency. Adapted from Wenger’s (1998) framework, VCoPs are defined as online groups united by a shared purpose, such as enhancing English pronunciation in this study, where members sustain knowledge co-construction through technology-mediated, often asynchronous, interaction. Among diverse VCoPs, YouTube has undergone a remarkable transformation from a traditional entertainment hub into a multifaceted educational platform (Barry et al., 2016; Bonfield et al., 2020; Duffy, 2008; Lee, 2021).

This study aims to analyze the sentiment expressed in YouTube comments about the English learning experiences—ranging from frustration to epiphany—in Oliver Ssam (2022) and Moon Ga Young (2023)’s videos¹, leveraging advanced sentiment analysis techniques employing Python and Orange 3, grounded in second language acquisition (SLA) theories such as the affective filter (Krashen, 1982), corrective feedback (Lyster & Ranta, 1997), and willingness to communicate (MacIntyre et al., 1998). By leveraging these theories, this study seeks to uncover how sentiment analysis can illuminate learners’ perceptions regarding their educational journeys of English learning. Furthermore, it intends to evaluate the role of digital platforms as a catalyst for creating interactive and engaging English learning experiences, fostering a sense of informal online community among learners, and enhancing their overall educational engagement. Interactive and collaborative informal learning communities refer to digital spaces where learners voluntarily participate in reciprocal exchanges and joint problem-solving without formal instructional oversight (Benson, 2015). Interactivity manifests through asynchronous comment-reply threads, where users pose questions (e.g., “How do you say ‘focus’ with [ou]?”) and receive responses, fostering dialogue. Collaboration occurs as learners collectively tackle linguistic issues, such as when Korean users explain the absence of diphthongs in their native phonology and suggest practice strategies, creating a supportive, peer-driven learning environment. In this study, these communities are exemplified by YouTube comment sections where Korean and global learners engage in discussions that blend linguistic inquiry with sentiment, such as 1) positive emotions (“jeong”); 2) negative emotions (“han”); 3) realization; 4) ambivalent emotions; and 5) tolerance expressed in the YouTube comments. This study aims to bridge the gap by analyzing YouTube comment sections as digital spaces for informal English learning, where learners engage in discussions, exchange insights, and develop linguistic proficiency within a community-driven environment of digital spaces, employing sentiment analysis of YouTube comment on English learning to offer methodologically and pedagogical implications for integrating VCoPs into English education.

Education has experienced notable transformations fueled by technological advancements and the increasing integration of digital platforms. Studies indicate that YouTube, social media, and OTT platforms significantly contribute to informal learning by offering access to a vast array of user-generated content. These platforms enable learners to engage with content in personalized online learning and raise a “sense of community and presence” (Johnson et al., 2023, p. 373) by participating in discussions, accessing real-world applications, and enhancing their understanding through diverse perspectives. In contrast to traditional classroom settings, where a standardized curriculum often governs learning, digital platforms promote self-directed learning, allowing students to select content that aligns with their interests and skill levels. YouTube, in particular, has proven to be highly effective in supplementing formal education, offering accessible and engaging language learning resources that cater to various skill levels. The platform’s recommendation algorithms further personalize the learning experience by suggesting relevant videos based on user’s preferences and previous interactions (Shoufan & Mohamed, 2022; Wang & Chen, 2020).

The concept of online communities of practice has gained prominence in the digital age (see Figure 1, Wenger, 2010, as cited in Bates, 2014). According to Wenger (2015), YouTube is positioned within the online learning domain of communities of practice (CoPs) in Figure 1. As a VCoP tool, YouTube facilitates the sharing of personal experiences and expertise, particularly in the context of English language learning. It serves as a virtual platform where learners share insights on English pronunciation and multilingual proficiency. Through active participation in video comment sections, users engage in informal learning and alignment within a VCoP, contributing to a dynamic and interactive learning community. These communities enable educators and learners to engage in continuous professional development and knowledge sharing beyond traditional classroom boundaries. In such informal online communities, participants can enhance their digital literacy skills as well as language learning and adapt to evolving educational technologies (Johnson & Majewska, 2022). CoPs consist of individuals connected by shared concerns, interests, challenges, or passion for a specific topic, fostering learning and expertise development through continuous interaction (Wenger, 2010; Wenger et al., 2002). While many CoPs emerge informally, formal recognition is more likely when they are strategically utilized within organizations. The advent of the internet and information and communication technologies (ICT) has expanded CoPs beyond geographical constraints, facilitating the rise of VCoPs (Wenger, 2015). VCoPs are defined as technology-supported communities that enable knowledge sharing and collective learning among geographically dispersed members (Ardichvili et al., 2003; Dubé et al., 2005). Many traditional CoPs have transitioned into online environments, while new VCoPs continue to emerge as organizations and industries become increasingly virtual. Research suggests that while tacit knowledge sharing—central to CoP theory—occurs in VCoPs, it may be less effective than in traditional CoPs (King, 1996).

Incorporating digital platforms like YouTube, social media, metaverse environments, and OTT media services into educational settings has transformed traditional learning experiences. These platforms offer interactive and immersive opportunities that cater to diverse learning preferences. For instance, Duffy (2008) highlighted the educational potential of YouTube, emphasizing its capacity to provide accessible and engaging content. Similarly, Wang and Chen (2019) pointed out that Taiwanese EFL students used YouTube to supplement their English learning, valuing its flexibility and engagement but recognizing its limitations for exam preparation. They suggest integrating YouTube content with formal instruction for a more effective learning experience. Barry et al. (2016) found that the “YouTube Generation” or “Generation Connected” (Gen C), primarily Millennials, increasingly integrates social media into anatomy education. Their study revealed that 78% of medical and radiation therapy students used YouTube for anatomy learning, highlighting the need for blended learning approaches with academic and ethical guidance. Chopra (2025) explores how OTT platforms, traditionally used for entertainment, can enhance student learning experiences. Analyzing interviews with 35 undergraduate and postgraduate students from various countries, this study identifies how OTT platforms can offer more engaging, personalized, and interactive learning experiences compared to traditional digital learning tools. Im and Park (2025) show that vlogs foster Korean EFL pre-service teachers’ translanguaging practices and multimodal literacy. Furthermore, immersive technologies such as the metaverse provide experiential learning experiences (Dede, 2020; Dede & Richards, 2012; Lee & Hwang, 2023).

Research on English language learning from the late 1980s to the early 2000s has highlighted how successful language learners employ both direct and indirect strategies, categorized as cognitive, metacognitive, and socio-affective approaches (O’Malley & Chamot, 1990; Oxford, 1990; Weinstein & Mayer, 1986; Wenden & Rubin, 1987). In addition to cognitive factors, affective influences play a crucial role in second language acquisition (SLA). Krashen’s (1985) Affective Filter Hypothesis suggests that motivation, attitude, anxiety, and self-esteem can either facilitate or hinder language learning. A lower affective filter, characterized by strong self-esteem, a clear study purpose, and manageable anxiety, allows for more significant language input, whereas a higher affective filter can impede learning. Similarly, Stern (1983) emphasized that affective factors are as significant as cognitive ones in SLA. Previous studies have primarily focused on language learners’ negative emotions, such as anxiety and nervousness, and language learning strategies aimed at achieving native-like proficiency. However, this study explores how VCoPs and digital environments such as YouTube have transformed English learning settings for language learners. These online communities play a pivotal role in facilitating learning English pronunciation through authentic materials, such as YouTube videos, while encouraging learners to participate actively. By leaving comments and engaging in discussions, EFL learners and users become active contributors to the VCoP, fostering collaborative learning and deeper engagement with pronunciation and language acquisition. This study will conduct a sentiment analysis of comments on two YouTube video clips (Moon Ga Young and Oliver Ssam) related to English pronunciation learning, providing insights into learners’ emotional responses and engagement in the VCoP. The following research questions guide this study:

1. How do sentiment analysis techniques applied to YouTube comments reflect learners’ perceptions and experiences of English learning through digital platforms?

2. What are the predominant emotional tones found in YouTube comments on English learning videos, and how do they influence engagement and learning outcomes?

3. How do social media platforms, including YouTube, facilitate dynamic engagement and personalized English learning experiences in informal VCoP for English learners, and what are the pedagogical implications for formal and non-formal language teaching stakeholders?

VADER algorithm was used for comments of two YouTube clips (Moon Ga Young; Oliver Ssam). Table 1 shows the results of Sentiment VADER for two videos. Both clips showed more positive comments than negative comments. Figures 2 and 3 imply column charts for high rates of positive sentiment in both clips, but the rate of negative sentiment is much higher in Oliver Ssam’s than in Moon Ga Young’s.

All the sentiment distributions of Oliver Ssam’s clip are in Figure 4. The positive score ranges from 0.0 to 1.0. It reveals most of the positive comments in Oliver Ssam’s are low positive sentiment scores. For example, one comment on YouTube says, “Dedicated English teacher taught English featuring family members long time concept teaching lot Koreans want to learn accurate English pronunciation learned channel irreplaceable,” with a positive score of 0.177, a negative score of 0.0, a neutral score of 0.823, and a compound score of 0.5106. The average positive sentiment score is 0.1461. The negative sentiment distribution of Oliver Ssam’s clip ranges from 0.0 to 1.0. It shows a similarity to the positive sentiment distribution with a prevalence of low negative sentiment scores. An example of a low negative sentiment score is “The difficulty level of English is extremely high because there are no principles of pronunciation.” Its negative score is 0.261, its positive score is 0.0, the neutral score is 0.739, and the compound score is -0.5574. The average negative score is 0.1070. The mean of the neutral score is 0.7469, and it implies neither extremely positive nor negative. The compound score ranges from -0.9958 to 0.9965, and the average score is 0.0669.

Figure 5 shows the four sentiment distributions of Moon Ga Young’s clip. The positive score ranges between 0.0 and 1.0. It demonstrates that the majority of Moon Ga Young’s positive comments have low positive sentiment scores. For instance, “I thought it was just me, but I feel somewhat comforted when I hear this logic. When you learn and use a certain language a lot, you gradually forget the language you knew before” illustrates a positive score of 0.173, a negative score of 0.069, a neutral score of 0.759, and a compound score of 0.5515. The mean positive score of 0.2323 is higher than that of Oliver Ssam’s clip, which has a score of 0.1461.

Similar to positive sentiment distribution, there is a preponderance of low negative sentiment scores in negative sentiment distribution. It ranges from 0.0 to 1.0, and one of the comment examples is, “The perspective on education may differ from person to person, but can you say it’s bad? There are many people who are full of holes even in just one language, so I don’t know if it’s possible to generalize because it’s different for everyone depending on the degree.” It indicates a negative score of 0.094, a positive score of 0.0, a neutral score of 0.906, and a compound score of -0.6956. The average negative score in Moon Ga Young’s clip is 0.0581, lower than the 0.1070 shown in Oliver Ssam’s clip. The neutral score has a mean of 0.7096, which reflects that it is neither very positive nor very negative. The average compound score is 0.3033, with a range of -0.9831 to 0.9972.

Table 2 indicates the confusion matrix linked to the Naïve Bayes classifier’s application to the test data. In Oliver Ssam’s clip, the accuracy of the classifier is 94%. Out of 2895 positive comments, 2808 were predicted correctly as positive. With 2259 negative comments, 2169 were correctly predicted as negative. In Moon Ga Young’s clip, the accuracy is 96%, and out of 694 positive comments, all 694 comments were predicted correctly as positive. Of 174 negative comments, 162 were correctly predicted as negative.

The researchers iteratively reviewed the comments and sentiment analysis results until they reached a consensus on five key themes related to two YouTube video clips. A qualitative research approach was employed to analyze how actual comments express emotions to complement the quantitative findings discussed above. The first clip featured an anecdote from Oliver’s family, a bilingual and bicultural content creator, about how the English word ‘focus’ was misheard as the swear phrase “f**k us” within his family. The second video showcased a multilingual Korean actress, Moon Ga Young, in a TV show episode that panelists praised for her fluency in Korean, German, and English. The comments in these two YouTube video clips reflected English learners’ diverse ways of expressing ambivalent feelings regarding English, discussing English education, sharing personal language learning experiences, and offering and sharing constructive feedback on this informal online community of practice (Wenger, 2010). The five key themes that emerged from the content analysis are as follows: 1. Positive emotions “jeong”; 2. Negative emotions “han”; 3. Realization: constructive feedback; 4. Ambivalent emotions: socioeconomic status; and 5. Tolerance: global English and English as a lingua franca.

This study reveals two key implications methodologically and pedagogically against the backdrop of its findings. Firstly, the sentiment analysis on VCoP highlights a novel approach to leveraging VCoP for English education. The active engagement of users in video comment sections fosters informal learning and alignment within VCoP, thereby cultivating a dynamic and interactive learning environment. These communities facilitate continuous professional development and knowledge sharing among educators and learners, extending beyond the confines of traditional classrooms. Moreover, participation in such informal online communities enhances participants’ digital literacy skills, supports language acquisition, and enables adaptation to emerging educational technologies (Johnson & Majewska, 2022)

Second, this study demonstrates the pedagogical potential of VCoP for Korean English learners, with sentiment analysis of YouTube comments revealing diverse emotional drivers-positive “jeong,” provocative “han,” neutral engagement “realization,” ambivalent socioeconomic reflections of learning English, and tolerance for multilinguals’ English accents—that may enhance English pronunciation learning through high engagement and interactivity on digital platforms, particularly when addressing authentic English productive skills (Lee, 2021; Lee et al., 2023). This participatory culture facilitates the development of virtual learning communities, where individuals collaboratively negotiate meaning and contribute to a broader discourse on digital literacy and education, serving as language educational content venues and fostering interactive language learning environments (Pires et al., 2022; Tan, 2013). These findings may inform targeted English teaching strategies, including structured YouTube comment prompts to cultivate “jeong” to inspire and improve peer support for English pronunciation practice, teacher-facilitated discussions on Learning Management Systems (LMS) to reflect on neutral “realization” sentiments as meta-cognitive language learning strategy, and exposure activities using free YouTube videos featuring diverse English accents to foster tolerance for global English. These language teaching strategies may promote immersive learning by combining visual, audio, and textual elements of digital resources for English language learning, leading to better reading and speaking production (Gilakjani, 2012). Ong and Quek (2023) found that improving these interactions is crucial, as their mixed-methods study of secondary students highlighted the importance of social engagement and effective teaching strategies in online classrooms. Personalization is also a key factor, as students prefer the ability to customize content to their preferences and provide feedback on their learning progress. This research found that YouTube comments created VCoP beyond the classroom for language learning. However, the shift to online learning has reduced teacher-student interactions, which may contribute to ineffective learning. According to Shoufan and Mohamed (2022), their systematic review of YouTube in education highlights concerns about content quality and the lack of a direct correlation between production strategies and learning outcomes. While most studies emphasize YouTube’s role as an educational tool, offering free, accessible, and engaging resources, they also underscore its challenges. These challenges can be better addressed by integrating YouTube into a structured, pedagogy-driven language learning framework. As the boundary between formal and informal language learning continues to blur (Benson, 2011, 2017; Sockett, 2014), English teachers, acting as guides and facilitators, can mitigate these challenges by delivering instruction within a guided learning environment, fostering more effective language learning design, even in informal contexts. Therefore, active engagement and interaction within VCoPs for language learning, supported by teachers as guides and facilitators, can enhance collaborative knowledge-sharing, foster deeper language acquisition, and leverage students’ affective factors and sentiment analysis results to optimize learning outcomes.

This study is not without limitations. The anonymity of YouTube commenters results in a lack of demographic information, including gender, age, and nationality. As YouTube comments are not a form of an interview, the analysis might be interpreted without confirmation to verify the commenters’ emotions or intentions. Furthermore, though public, YouTube comments are often posted with an expectation of limited visibility within the platform’s community, not as data for academic research (Fiesler, 2019). This study anonymized all data by removing usernames to protect commenters’ privacy, but the assumption that public posting implies consent is ethically contentious. In an ethical stance in analyzing YouTube comments, the absence of explicit consent remains a limitation, highlighting the need for clearer guidelines on social media data use in research (Tanner et al., 2023). Moreover, machine-translated Korean YouTube comments introduce limitations, as the English-based “monologg/bert-base-cased-goemotions-original” model may not fully capture cultural nuances or emotional subtleties altered during translation. Errors in translating context-specific Korean expressions, such as honorifics or idioms, could affect the model’s emotion classification accuracy. To address this, a reliable translation tool was used, but future studies could employ Korean-specific models or bilingual datasets to enhance performance.

Future studies could extend this research by measuring informal second language engagement using Arndt’s (2023) questionnaire, alongside pre- and post-tests, to assess the impact of implementing informal English learning digital platforms in formal English classrooms. Furthermore, it illustrated that YouTube comments can serve as an authentic and effective interpersonal platform for accessing diverse sentiments. A key advantage of this sentiment analysis research is its ability to facilitate active engagement among commenters on informal platforms, which can be effectively integrated into formal educational settings (Lin, 2022). In an era where the boundaries between informal and non-formal education are becoming increasingly blurred, VCoP emerges as a powerful tool and may provide an easily accessible and voluntary environment for English education, offering a unique opportunity for learners to engage in language learning in a flexible and self-directed manner.