Analysis of YouTube video contents on Koha and DSpace, and sentiment analysis of viewers' comments

Abstract

PurposeThe purpose of the study is to evaluate certain characteristics of the videos of the software Koha and DSpace posted on YouTube. Since YouTube has the potential to provide the content creator to share their knowledge and experience through their content which has become much more beneficial to the information seeker. Nowadays, people search for queries or tutorial videos on YouTube very often to earn a better understanding of the term. Sentiment analysis of the viewers' opinion of the videos is another purpose of this study.Design/methodology/approachDataset for evaluating the characteristic of the videos of Koha and DSpace was extracted by using Webometric Analyst by creating YouTube API. Once retrieval of data was completed, a manual verification was enhanced to filter out spam videos unrelated to the scope. After the confirmation of authentic relatable videos, seeking the video's id as query, the comments per video were extracted using Webometric Analyst. For opinion mining, the Parallel Dots API web service was used in Google Sheets as an addon function. The sentiment, multilingual sentiment, emotion, intention and word frequency of the viewers' opinion was examined with the help of certain default functionalities.FindingsWebometric Analyst extracted a total of 461 and 397 videos of Koha and DSpace, respectively, uploaded on the YouTube platform. The findings of the study indicate that the growth rate of videos on Koha is decreasing, while the number of videos uploaded on DSpace is gradually increased in the last 10 years. The highest number of videos posted in 1–20 min duration category with mostly high definition (HD) with standard YouTube license and prominently in the English language. The sentiment analysis of the total extracted comments on Koha and DSpace videos found to be 2043 and 862 comments, respectively, among whom “Positive” comments are mostly found and with “Happy” emotion can be highly detected with most supportive “Feedback” intention on both Koha and DSpace videos. The top word frequency signifies that the users of both the software are using the comments section of the videos on YouTube to ask and provide troubleshooting help to each other.Research limitations/implicationsThe present study has some limitations too; the dataset for the study includes only those videos whose title, description or keywords sections had the query terms “Koha” or “DSpace” there are chances that some videos would have been left out from the dataset related to these software.Originality/valueThis is the first paper to evaluate the characteristics and sentiment of both the videos Koha and DSpace. Through this, the popularity, likeness and dislike and the impact of the contents of the videos uploaded will be disclosed, and creators can make an improvement by referring this, and the seekers will adapt to the use of correct and authentic information.

Editorial: Special selections on contemporary social media and social network research

“…They noted that forum comments about the videos were related to the content communities instead of the video content. Deori et al . (2023) also studied videos uploaded to YouTube.…”

“…They noted that forum comments about the videos were related to the content communities instead of the video content. Deori et al (2023) also studied videos uploaded to YouTube. They focused on studying those videos prepared using Koha and DSpace using Webometric Analyst and Parallel Dots API for data analyses.…”

What news sparks interest on YouTube? A study of news content uploaded by India's top five Hindi news networks

“…Several studies have been published that demonstrate the use of sentiment analysis on social media material to explore intents, video qualities and future research possibilities. Deori et al (2021) analysed the sentiment study of the comments on the videos on Koha and DSpace software on YouTube using paralleldots application programming interface (API) and evaluated the sentiment differentiating in both English language and multilingual format. The content of the paper reveals the characteristics of the videos extracted and the intention of the comments referring to the end-users to adopt the use of correct and relevant information.…”

Sentiment analysis of online discussion of LIS professionals using R

“…The study found that the users of the Android version of the Mendeley app are more satisfied than the iOS version, despite both having the same features. Deori et al (2021) analyzed the opinion shared by the YouTube viewers on the video of Dspace and Koha. The author used the Parallel Dots API in Google Sheets for mining the opinion.…”

“…Deori et al (2021) analyzed the opinion shared by the YouTube viewers on the video of Dspace and Koha. The author used the Parallel Dots API in Google Sheets for mining the opinion.…”