Before vectorization, text data requires several preprocessing steps to convert raw input into a structured format suitable for machine learning models. These steps ensure consistency, reduce noise, and improve the quality of numerical representations. The exact steps depend on the application, but common practices include cleaning, normalization, and structural adjustments to prepare text for algorithms like TF-IDF, word embeddings, or bag-of-words models.
First, basic cleaning removes irrelevant characters and standardizes text. This includes lowercasing all text to eliminate case sensitivity (e.g., converting “Cat” and “cat” to the same token), stripping punctuation or special symbols (like commas or hashtags), and filtering out numbers or non-printable characters. Tokenization splits text into individual units (words, subwords, or phrases) using libraries like NLTK or spaCy. For example, the sentence “I’m loving this!” becomes ["i’m", "loving", “this”]. Stop word removal eliminates common but low-meaning words (e.g., “the,” “and”) using predefined lists, though this step is optional—some tasks, like sentiment analysis, may retain stop words for context. Handling HTML tags, URLs, or emojis (e.g., replacing 😊 with “happy_emoji”) is also part of cleaning, especially for web data.
Next, normalization ensures linguistic consistency. Stemming and lemmatization reduce words to their root forms. For instance, stemming converts “running” to “run,” while lemmatization maps “better” to “good” using lexical databases like WordNet. Handling contractions (e.g., expanding “don’t” to “do not”) and correcting typos (via dictionaries or tools like SymSpell) improves uniformity. Encoding normalization, such as converting accented characters (é to e) or unifying Unicode formats, prevents duplicate representations. Case sensitivity and domain-specific terms (e.g., replacing “COVID-19” with “coronavirus”) may also be addressed here. For example, in a medical dataset, “heart attack” and “myocardial infarction” might be standardized to a single term.
Finally, structural adjustments tailor the text to the model’s needs. N-gram extraction identifies frequent word pairs (e.g., “machine learning” as a single token) to capture context. Handling rare or frequent words—removing terms below a frequency threshold or capping overly common ones—reduces noise. Custom rules, like preserving product names or handling hashtags (e.g., #AI → “ai_hashtag”), can be applied. For languages with complex morphology, like Arabic or German, additional steps like segmentation may be required. It’s crucial to validate steps against the use case: removing punctuation might harm a grammar-checking model, while lowercasing could lose critical context in entity recognition. Tools like spaCy’s language pipelines or custom regex rules help automate these steps efficiently.
In summary, preprocessing involves cleaning, normalizing, and restructuring text to balance consistency with task-specific needs. Developers should iterate on these steps based on model performance and domain requirements.