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I did that in 2012 for a pet project with a friend https://github.com/SnippyHolloW/HN_stats https://github.com/SnippyHolloW/HN_stats Here is the trained topic
by snippyhollow 12y ago
I did that in 2012 for a pet project with a friend https://github.com/SnippyHolloW/HN_stats https://github.com/SnippyHolloW/HN_stats
Here is the trained topic model (Nov. 30, 2012) with only 40 topics (for file-size mainly) https://dl.dropboxusercontent.com/u/14035465/hn40_lemmatized.ldamodel https://dl.dropboxusercontent.com/u/14035465/hn40_lemmatized...
You can load it with Python:
from gensim.models import ldamodel
lda = ldamodel.LdaModel.load("hn40_lemmatized.ldamodel")
lda.alpha = [lda.alpha for _ in range(40)] # because there was a change since 2012
lda.show_topics()
Now if you can figure out what is this file:
https://dl.dropboxusercontent.com/u/14035465/pg40.params https://dl.dropboxusercontent.com/u/14035465/pg40.params I'll pay you a beer next time you're in Paris or I'm in the Valley. ;-)
- doppenhe 12y agoI'll take a look, thanks!
- sitkack 12y ago[0.006791154078718692, 0.004654721825624361, 0.004632114646875695, 0.011976800134937546, 0.01799155954072435, 0.009181094647452455, 0.0345230793213232, 0.005232498042562552, 0.011402661654834138, 0.009024477282685779, 0.007034922780349653, 0.0031922239118904504, 0.007097905854058182, 0.004999249488505551, 0.016499595508879424, 0.024729974527642036, 0.004985711413178751, 0.03119529793092641, 0.015437847520669401, 0.2948424084650949, 0.06912364384956156, 0.004776347484051836, 0.0893067258386264, 0.018226129712679208, 0.0315656235097838, 0.006267920316323028, 0.01240414536928756, 0.005343403072840281, 0.006566103139036195, 0.009403510178615212, 0.009875448474490003, 0.0038449507757111973, 0.007531241580033292, 0.0014680865916836157, 0.00767397135040071, 0.002118254148078463, 0.02605710351719099, 0.034716581721697254, 0.002314474872742398, 0.12599103592023264]
- snippyhollow 12y agoYes but what does it mean?
- sitkack 12y agoHaving not read the code, I'd wager it is a 40 element vector of term similarities.
- waterside81 12y agoLooks like cosine similarity output
- snippyhollow 12y agoIt's PG's parameters for a naive Bayes model based on these LDA topics, learned by taking his comments on HN as upvotes for the articles content.