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I'm attempting to classify text documents using a few different dimensions. I'm trying to create arbitrary topics to classify such as size and relevance, which are linear or gradual in nature. For example:

size: tiny, small, medium, large, huge. relevance: bad, ok, good, excellent, awesome

I am training the classifier by hand. For example, this document represents a 'small' thing, this other document is discussing a 'large' thing. When I try multi-label or multi-class SVM for this it does not work well and it also logically doesn't make sense.

Which model should I use that would help me predict this linear type of data? I use scikit-learn presently with a tfidf vector of the words.

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  • $\begingroup$ maybe I just need to add another vector in addition to the words...? but that seems odd. $\endgroup$
    – dwenaus
    Commented Sep 17, 2015 at 18:31

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If you want these output dimensions to be continuous, simply convert your size and relevance metrics to real-valued targets. Then you can perform regression instead of classification, using any of a variety of models. You could even attempt to train a multi target neural net to predict all of these outputs at once.

Additionally, you might consider first using a topic model such as LDA as your feature space.

Based on the values, it sounds like the "relevance" might be a variable best captured by techniques from sentiment analysis.

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  • $\begingroup$ can you give examples of how one would perform regression on a bag of words? $\endgroup$
    – dwenaus
    Commented Sep 17, 2015 at 19:25
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    $\begingroup$ 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? $\endgroup$
    – jamesmf
    Commented Sep 17, 2015 at 19:29
  • $\begingroup$ 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? $\endgroup$
    – dwenaus
    Commented Sep 17, 2015 at 19:58
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    $\begingroup$ 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). $\endgroup$
    – jamesmf
    Commented Sep 17, 2015 at 20:17

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