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Temporal Analysis on Topics Using Word2Vec. (arXiv:2209.11717v1 [cs.CL])
Sept. 26, 2022, 1:15 a.m. | Angad Sandhu, Aneesh Edara, Faizan Wajid, Ashok Agrawala
cs.CL updates on arXiv.org arxiv.org
The present study proposes a novel method of trend detection and
visualization - more specifically, modeling the change in a topic over time.
Where current models used for the identification and visualization of trends
only convey the popularity of a singular word based on stochastic counting of
usage, the approach in the present study illustrates the popularity and
direction that a topic is moving in. The direction in this case is a distinct
subtopic within the selected corpus. Such trends …
More from arxiv.org / cs.CL updates on arXiv.org
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