- How I Improved My Text Classification Model With
6 days ago Broke the documents in list of words.Removed stop words, punctuations.Performed stemming.Replaced numerical values with '#num#' to reduce vocabulary size.Transformed the documents into TF-IDF vectors.Sorted all words based on their TF-IDF value and selected the top 20K words, these will be used a feature list for the classification algorithm.Used SVM.
1. Broke the documents in list of words.
2. Removed stop words, punctuations.
3. Performed stemming.
4. Replaced numerical values with '#num#' to reduce vocabulary size.
5. Transformed the documents into TF-IDF vectors.
6. Sorted all words based on their TF-IDF value and selected the top 20K words, these will be used a feature list for the classification algorithm.
7. Used SVM.
1 week ago Web How I improved my text classification model with feature engineering | by Alexandre Wrg | Towards Data Science 500 Apologies, but something went wrong on our end. Refresh the page, check Medium ’s site status, or find something interesting to read.
4 days ago Web May 27, 2021 · Generalising to unseen datasets is the goal of a classification model. Typical text classification models in the security domain are usually constructed based on an oracle dataset (Nigam et al., 2000) containing experts’ (human/system) classifications of a sample of the population that a classification algorithm should learn. The requirement ...