<ul><li>Text preprocessing is a crucial step in NLP projects to ensure model performance.</li><li>Lowercasing text helps maintain consistency and reduce vocabulary size.</li><li>Removing HTML tags, URLs, punctuation, and informal words enhances data quality.</li><li>Spell correction tools like TextBlob are used to rectify common mistakes.</li><li>Removing stop words can assist in improving processing speed and reducing tokens.</li><li>Handling emojis based on task requirements can influence sentiment analysis results.</li><li>Tokenization is essential to split text accurately for model understanding.</li><li>Methods like split(), regex, and libraries like NLTK and SpaCy are used for tokenization.</li><li>Stemming helps reduce words to their root form, useful for information retrieval systems.</li><li>Porter Stemmer and Snowball Stemmer from NLTK are commonly used for stemming.</li><li>Lemmatization ensures proper reduction of inflected words to their base form using WordNet.</li></ul>

Why Most NLP Projects Fail: A Beginner’s Guide to Text Preprocessing

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