My colleagues and I recently presented and published research at EDULEARN 2022 titled, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”. The paper presents on differences in online interaction before and during COVID-19 lockdowns with an emphasis on sentiment analysis. Below is the tag cloud, abstract and full citation of the research.
In early 2020, there was a drastic shift in the teaching modality across institutions of higher education as colleges and universities adapted to federal guidelines in the face of COVID-19 restrictions. While most, if not all institutions already had mature learning management systems (LMS), alternative approaches using social components proved invaluable during a time when face-to-face social interaction was largely restricted. More so, a large concern in the shift to remote instruction during COVID-19 lockdowns was the toll on student well-being as traditional learning management systems typically lack social networking capabilities. In this paper, we explore the use of online social networking (OSN) software to facilitate more positive interactions. More specifically, we analyse the sentiment within online discussions to show key differences between users using traditional LMS software and those using modified OSN software. Findings show that students participating during the pandemic, yielded higher levels of social presence and posted more positively to the discussion.
Thoms, E. Eryilmaz, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”, 14th Annual International Conference on Education and New Learning Technologies (EDULEARN 22), July 5, 2022.