In this post we would look at how to handle text data in python. Any text analysis activity basically has three main components:
We would look at a small text example and understand how to perform the above three steps using the nltk library. I have performed all the operation by downloading all the methods in nltk using the following line of code
I have not mentioned the above line of code in the attached python notebook and html version but it is advisable for users to run the above line after doing import nltk. The nltk.download() will take some time (few hours) to download all the relevant packages to your console. After this you can run the entire python script.
Download Link: https://drive.google.com/drive/folders/12LrZTI5qT-vzz6ce5dpXZ2ucdUsfa9S_?usp=sharing
Download the ipynb file and html version to understand the flow
- Tokenization
- Lemmatization/Stemming
- Stop Word Removal
We would look at a small text example and understand how to perform the above three steps using the nltk library. I have performed all the operation by downloading all the methods in nltk using the following line of code
- nltk.download()
I have not mentioned the above line of code in the attached python notebook and html version but it is advisable for users to run the above line after doing import nltk. The nltk.download() will take some time (few hours) to download all the relevant packages to your console. After this you can run the entire python script.
Download Link: https://drive.google.com/drive/folders/12LrZTI5qT-vzz6ce5dpXZ2ucdUsfa9S_?usp=sharing
Download the ipynb file and html version to understand the flow
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