Text Cleaning & Noise Removal
Make the same word look the same
To a computer, "Movie", "movie", and "movie!!!" are three different things. Cleaning normalizes surface variation so they all collapse to one token.
Cut the noise and the vocabulary shrinks, counts line up, and the model can focus on signal instead of formatting quirks.
Scrub a messy review
Watch a raw, noisy review get lowercased, stripped of HTML and URLs, cleared of punctuation, and tidied of extra whitespace.
The usual operations
Collapse case so the same word isn't counted twice.
Remove HTML tags, links, and other boilerplate that carries no meaning.
Remove symbols (for bag-of-words models). Numbers too, if they don't matter.
Collapse runs of spaces, tabs, and newlines into single spaces.
Optionally fold accents or convert emojis to words, depending on the task.
Clean carefully — not everything is noise
- HTML tags and URLs
- Extra whitespace
- Case differences (for most tasks)
- Punctuation — "!" can signal strong sentiment
- Emojis — often carry the sentiment in social text
- Negation — "not good" must keep the "not"
- For transformers, heavy cleaning often hurts
After cleaning, the next normalization steps are Stop Words, Stemming, and Lemmatization.