Handling Noisy Data

Description

Real-world text data is often noisy, containing misspellings, grammatical errors, or irrelevant information.

To handle noisy data, consider the following strategies:

• Preprocessing: Clean the text data by correcting misspellings, removing special characters, expanding contractions, and converting text into lowercase
• Stopword removal: Remove common words that do not carry much meaning, such as "the," "is," "and," and so on
• Stemming or lemmatization: Reduce words to their root form to minimize the impact of morphological variations
• Feature selection: Use techniques such as chi-square or mutual information to select the most informative features, reducing the impact of noisy or irrelevant features