This Python script preprocesses text data using NLTK (Natural Language Toolkit) for tasks such as tokenization, stop word removal, punctuation removal, and lemmatization.
The script preprocess_text.py reads text data from a file named data.txt and performs the following preprocessing steps:
- Tokenization: Splitting the text into individual words or tokens.
- Lowercasing: Converting all tokens to lowercase to ensure uniformity.
- Stopword Removal: Removing common words like "the", "is", "and", etc., which do not carry significant meaning.
- Punctuation Removal: Eliminating punctuation marks from the text.
- Lemmatization: Reducing words to their base or root form to handle variations like plurals, verb tenses, etc.
- HTML Tag Removal: Stripping HTML tags from the text to clean it from any markup.
The preprocessed data is stored as a list of lists, where each sublist contains the preprocessed tokens for each line of text in
data.txt.
- Clone this repository to your local machine:
git clone https://github.com/your-username/your-repository.git