YouTube is known as one of the largest online video repositories and is
used in many domains. However, even though it is commonly used for
educational purposes, the aims of an educator and YouTube may not
coincide. The educator wants learners to watch only what is needed and
proceed to their practical work. YouTube, on the other hand, focuses on
maximizing revenue. Therefore, videos with low popularity are generally
not being recommended. Such low-popularity videos may actually be
helpful to the learner had they been recommended. This is known as
serendipity or long-tail recommendation. This paper aims to first
understand how serendipitous YouTube recommendations are. Second, to
analyze how sentiment polarity affects recommendations. Third, to
provide a clustering and video recommendation as an alternative to
YouTube recommendations, focusing on learning. The result of this
research shows that, first, YouTube recommendations lack serendipity but
include sentiment along with other factors. Second, the sentiment
polarity has an effect on serendipity. The exclusion of dislike counts
would make the recommendation favor view counts. The inclusion of
dislike counts increases the serendipity factor in the recommendation
list. Third, the research utilizes k-means for video clustering with
like-to-dislike ratios. This feature recommends more serendipitous
videos than the default YouTube recommendation. These results improve
academic video recommendations on YouTube.