Timeline for Classifying text documents using linear/incremental topics
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 17, 2015 at 22:20 | vote | accept | dwenaus | ||
| Sep 17, 2015 at 20:17 | comment | added | jamesmf | Sure, tf-idf vectors might be sufficient. If you need your output labels to be ordered, you need to either assign real values to the targets (as I said above). In order to go from tf-idf vectors or topic vectors to your labels, you would either use any kind of regression (if you map labels to numbers) or a classifier (which it sounds like you've already tried). | |
| Sep 17, 2015 at 19:58 | comment | added | dwenaus | We're using python and scikit-learn. I've used LDA and LSA for topic modelling, but I'm not sure how that fits onto my labels. And what about just using a tfidf vector of the document words? | |
| Sep 17, 2015 at 19:29 | comment | added | jamesmf | If you used LDA for example, each document would be represented by a topic vector. You could then transform your labels ("big", "small", etc) into real-values (big=100,small=10,tiny=1) and perform any kind of regression, even simple linear regression, to predict the real-valued targets. There are a number of places you could get implementations of LDA. What language are you using? | |
| Sep 17, 2015 at 19:25 | comment | added | dwenaus | can you give examples of how one would perform regression on a bag of words? | |
| Sep 17, 2015 at 18:41 | history | answered | jamesmf | CC BY-SA 3.0 |