What type of data does Natural Language Processing typically focus on?

Natural Language Processing (NLP) primarily focuses on text and speech data because its main goal is to enable machines to understand, interpret, and respond to human language. This involves analyzing various forms of human communication, including written text and spoken language, to extract meaningful information and insights.

NLP techniques are employed in tasks such as sentiment analysis, language translation, speech recognition, and chatbots, all of which rely on processing language in its natural form. The inherent complexity of human language—such as syntax, semantics, and context—necessitates specialized approaches that are most effectively applied to text and speech, making them the central data types for NLP.

In contrast, numerical data pertains to figures or quantitative information, structured data refers to information organized in a predefined manner, like databases, and image data involves visual content, which are not the primary focuses of NLP.