Natural Language processing- Text preprocessing

"I’m currently working on a Natural Language Processing (NLP) project, and I’m facing challenges in the text preprocessing stage. Here’s what I’ve done so far:

  1. Converted all text to lowercase.
  2. Removed HTML tags, special characters, and punctuation.
  3. Applied stemming using Porter stemming algorithm.
  4. Utilized TF-IDF (Term Frequency-Inverse Document Frequency) vectorization.
  5. Converted categorical labels into one-hot encoding format.

Now, I’m seeking guidance on whether there are additional preprocessing steps I should consider. Additionally, I’d like to know if it’s appropriate to proceed with training the model using the data after implementing the aforementioned methods. Are there any standard procedures or best practices I should adhere to? If so, could you please provide guidance on what those are?"

You should check the techniques used in natural Language Processing Specialization.

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