Article Details

Abstract

As an interactive tool, the barrage has the characteristics of immediacy, diversity and anonymity of interaction, which can effectively alleviate the problem of insufficient interaction in learners' online learning process and improve learners' learning performance. In this study, Python is used to crawl the teaching video barrages on Bilibili, and LSTM network is used to predict the number of barrages. By using the SnowNLP library to analyze the emotion of barrages, it is found that barrages help to enhance the on-site presence and reduce the loneliness of learning. Teachers should pay attention to the number of barrages at different nodes, improve teaching videos, and create an equal and warm atmosphere with the help of barrages to reduce learners' anxiety.

Keywords

barrage; teaching video; quantity prediction; sentiment analysis

References

1. [1] Song Ningning, Yu Xinpei, Feng Xiaoying The impact of bullet comments on students' online video learning effectiveness: a meta-analysis based on 22 experimental and quasi experimental studies [J]. Open Learning Research, 2024, 29 (5): 23-32
2. [2] Yang Tianping, Li Hongmei Analysis of the popularity of danmu culture in Asia and the cold reception in Europe from a cross-cultural perspective. Journal of Hubei University of Economics: Humanities and Social Sciences Edition, 2021, 18 (8): 101-103
3. [3] Zhang Wenlan, Chen Lixing, Sun Mengyang What does bullet screen interaction bring to online learning for college students? ——Qualitative analysis based on grounded theory [J]. Modern Distance Education, 2022 (5): 12-19
4. [4] Zhao Tong, Li Qian, Wang Tianran The impact and development suggestions of video barrage in the digital age [J]. TV Research, 2022 (4): 107-109
5. [5] Yang Jiumin, Wu Changcheng, Pi Zhongling, etc Promoting or Interfering with Learning: A Meta Analysis of the Impact of Barrage on Learning [J]. Research on Electronic Education, 2019, 40 (6): 84-90+120
6. [6] Sun Xiaoning, Yao Qing Comparative study on user barrage and comment characteristics in diverse thematic scenarios: based on Bilibili website [J]. Intelligence Theory and Practice, 2021, 44 (9): 135-141+121
7. [7] Chen Yijin, Zhuo Linkai, Zhao Yiming Research on the Interactive Behavior of Learning Video Barrage Users [J]. Library Forum, 2021, 41 (9): 95-101+124
8. [8] Shen Hao, Liang Ying Exploration of Effective Strategies for Online Teaching: A Barrage Text Analysis Based on the Bilibili Online Open Course "Education" [J]. Research on Electronic Education, 2022, 43 (11): 69-76+84
9. [9] Wang Li Research on Feature Discovery and Emotion Analysis Based on Video Barrage [J]. Computer Technology and Development, 2022, 32 (1): 141-146
10. [10] Yu Weijie, Shi Yujun, Zhou Shengxin, etc Research on Dynamic User Profile Based on Barrage: A Case Study of Online Education [J]. Intelligence Science, 2024, 42 (3): 147-155

About the authors

Huang Lan (1989-), female, Han ethnicity, from Nanning, Guangxi, is a senior engineer with a master's degree. Her research interests include educational technology and big data analysis; He Peicai (2001-), male, Han nationality, from Rong County, Guangxi, undergraduate, research direction: big data analysis.