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View Code? Open in Web Editor NEWYet Another Topic Modeling Browser...
License: GNU General Public License v3.0
Yet Another Topic Modeling Browser...
License: GNU General Public License v3.0
We currently compute correlation based on frequency over time. We should also compute similarity based on document distribution by transposing the document-topic matrix.
Link to the PhiloLogic view instead
Rather than rebuild a new the document-topic matrix, use what sklearn outputs. This will simplify the code greatly.
Use masks to build doc-topic matrix for each year interval, and compute word similarity by doc co-occurrence and topic distribution.
See numpy implementation here:
https://en.wikipedia.org/wiki/Time_Warp_Edit_Distance
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