A clustering-based topic model using word networks and word embeddings
Online social networking services like Twitter are frequently used for discussions on numerous topics of interest, which range from mainstream and popular topics (e.g., music and movies) to niche and specialized topics (e.g., politics). Due to the popularity of such services, it is a challenging tas...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Subjects: | |
| Online Access: | View Fulltext in Publisher |