I am trying to use DBSMOTE(Density-Based Synthetic Oversampling TEqnique) to on a data set of short text--tweets to be specific. This will be used to train a classifier model in a multiclass classification model. This will be done at feature level augmentation, using TF-IDF as features. I have read though, that to use SMOTE on NLP, the feature vectors must be reduced in dimension. What is an optimal size of a feature vector to use in a SMOTE family algorithm?

Similar Question: How do you apply SMOTE on text classification?

DBSMOTE Code: https://rdrr.io/cran/smotefamily/man/DBSMOTE.html

DBSMOTE Paper: https://link.springer.com/article/10.1007/s10489-011-0287-y

  • $\begingroup$ Hey! It'd be great if you added a link to where you read that about SMOTE on NLP. That way, we can read further on the topic and try to help you on that. $\endgroup$
    – jmnavarro
    Commented May 15, 2018 at 22:42
  • $\begingroup$ Thanks for the tip @jmnavarro, I'm very new to using StackExchange $\endgroup$ Commented May 15, 2018 at 23:03


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