Natural Language Processing (NLP) is a branch of AI that focuses on the interaction between computers and human language. One of its most practical and widespread applications isTextual data cleaning. When dealing with large datasets of unstructured text—such as customer reviews, social media posts, or support tickets—the data is often "noisy," containing typos, slang, irrelevant HTML tags, or inconsistent formatting.

NLP algorithms are used to standardize this data through techniques like tokenization (breaking text into words), stemming or lemmatization (reducing words to their root form), and "stop word" removal (filtering out common words like "the" or "is" that don't add semantic value). This cleaning process is essential before any higher-level analysis, such as sentiment analysis or topic modeling, can take place. If the data isn't cleaned, the resulting AI model will be less accurate. Unlike "Numerical data cleaning" (Option D), which deals with outliers or missing values in numbers, textual data cleaning requires an understanding of linguistic rules and context, which is the core strength of NLP. Effective prompt engineering often involves asking an AI to perform these cleaning tasks to prepare a dataset for more complex reasoning or summarization.