Introduction to Natural Language Processing

Text Preprocessing

In the Introduction to NLP, we explored the broad field of Natural Language Processing and its various applications. To build effective NLP models, it's crucial to start with clean and well-prepared data.

Raw text data, while rich and informative, often contains inconsistencies, irrelevant information, and other challenges that can hinder model performance. Text preprocessing involves preparing and cleaning text data to make it suitable for analysis. Proper preprocessing can significantly improve the performance of NLP models by ensuring that the data fed into algorithms is consistent, structured, and free from noise.

Techniques and Tools

In order to allow machines to process our text data properly we should utilize these techniques

1. Tokenization:

   • Tokenization is the process of splitting text into smaller units called tokens, which can be words, phrases, or even individual characters.
   • Example: The sentence "This is a sample sentence." can be tokenized into ["This", "is", "a", "sample", "sentence"].

2. Removing Stop Words:

   • Stop words are common words that carry little meaning and can be removed from the text to reduce its dimensionality. Examples include "is," "and," "the," etc.
   • Example: Removing stop words from the sentence "This is a sample sentence." results in ["sample", "sentence"].

3. Stemming and Lemmatization:

   • Stemming involves reducing words to their base or root form by removing suffixes (e.g., "running" becomes "run").
   • Lemmatization is a more advanced technique that reduces words to their base or dictionary form (lemma), considering the word's context (e.g., "better" becomes "good").
   • Example: Stemming the sentence "The cats are running" might produce ["The", "cat", "are", "run"], while lemmatization might yield ["The", "cat", "are", "run"].

After all these steps are utilized the raw text can go from:

Raw Text:
"The service at the restaurant was okay. The food was good, but I think it could have been better."

After Text Preprocessing:
"service restaurant okay food good think could better"

These preprocessing steps help clean and normalize text data, making it more manageable and ready for further analysis. Proper text preprocessing would involve tokenizing, removing stop words like "the" and "is," and normalizing words to their root forms. This ensures that your model focuses on meaningful words that contribute to sentiment, ultimately improving the accuracy of predictions.