100 Things to Do For Text Processing and Analysis

Photo by Joshua Hoehne on Unsplash

Text preprocessing is a crucial step in natural language processing (NLP) that involves cleaning and transforming raw text data into a format suitable for analysis and machine learning. Here are 100 things to do for text processing and analysis:

1. Tokenization: Tokenization is the process of breaking text into words, phrases, symbols, or other meaningful elements, known as tokens.
2. Stop words: Stop words are common words (e.g., “the,” “is,” “and”) often removed during preprocessing to reduce noise.
3. Stemming: Stemming involves reducing words to their root or base form (e.g., “running” to “run”).
4. Lemmatization: Lemmatization is the process of reducing words to their base or dictionary form (e.g., “better” to “good”).
5. Lowercasing: Converting all text to lowercase helps ensure uniformity in word representation.
6. Noise removal: Removing irrelevant or unnecessary information, such as special characters, numbers, or HTML tags, is essential for cleaning text.
7. Whitespace removal: Eliminating extra spaces improves consistency and readability.
8. Handling contractions: Expanding contractions (e.g., “don’t” to “do not”) can aid in understanding.
9. Spell checking: Correcting…