Text Mining-Based Analysis of Content Topics and User Engagement in University Social Media
A large amount of text data flows through the web every day in the context of various comments and reactions of users to social network content. Such information tokens can act as quality metrics for content, a group, or an industry. This information can be valuable for decision makers and content m...
Saved in:
| Main Authors: | Mark Soloviev, Pavel Aksenov, Angi Skhvediani, Timur Tenishev, Fedor Kolomenskiy, Elena Bormontova |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10706227/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Topic Modeling for Makerspace Artifact Analysis
by: David Wilson, et al.
Published: (2021-04-01) -
Discovering topics and trends in biosecurity law research: A machine learning approach
by: Yang Liu
Published: (2025-06-01) -
Topic-Weighted Kernels: Text Kernels Integrating Topic Weights and Deep Word Embeddings for Semantic Text Analytics
by: Nikhil V. Chandran, et al.
Published: (2025-01-01) -
A topic modeling approach for analyzing and categorizing electronic healthcare documents in Afaan Oromo without label information
by: Etana Fikadu Dinsa, et al.
Published: (2024-12-01) -
Exploring Socioeconomic Challenges Using Latent Dirichlet Allocation and Text Mining: Convergence Points Between World Bank and The International Monetary Fund Reports
by: Yasmine Derradj, et al.
Published: (2025-03-01)