I'm performing Truncated SVD on a word co-occurrence matrix for topic extraction. I'm using the scikit-learn implementation.

I have read conflicting ideas on how to interpret the sign of the loadings in Truncated SVD. On the one hand, I've seen it suggested that a negative loading indicates a lack of that particular word in the topic/component. I've also seen it suggested that they are totally arbitrary.

My question is potentially two-fold:

  1. Is the sign meaningless? I.e., does it not indicate the lack of a word.
  2. If the sign is meaningless, how do I interpret and use the loadings to identify topics?

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