Preprocessing Text Classification

Preprocessing in text classification is the set of steps applied to raw text before feeding it into a machine learning or deep learning model. The goal is to normalize text, remove noise, and convert it into a numerical representation suitable for algorithms.

Typical Preprocessing Steps

Text Cleaning

• Remove punctuation, special characters, numbers (optional).
• Lowercasing text: "The Cat" → "the cat".
• Remove unwanted whitespace.

Tokenization

• Split text into tokens (words, subwords, or characters).

  • Example: "I love NLP" → ["I", "love", "NLP"].

Stopword Removal

• Remove common words that add little meaning:

  • Example: "is", "and", "the".

Normalization

• Stemming: Reduce words to their root form (e.g., "running" → "run").
• Lemmatization: Use vocabulary and grammar to reduce to base form ("better" → "good").

Handling Categorical/Text Variants

• Remove URLs, HTML tags, mentions (@username), hashtags.

Handling Out-of-Vocabulary (OOV) & Rare Words

• Replace rare words with <UNK> token or use subword tokenization (e.g., BPE).

Encoding Text

Convert processed tokens into numerical format:

• Bag of Words (BoW)
• TF-IDF
• Word Embeddings (Word2Vec, GloVe, FastText)
• Contextual embeddings (BERT, GPT tokenization).