Textual Analysis

Textual analysis, also known as text mining, is a discipline at the intersection of Computer Science and Data Science that involves using computational and statistical techniques to extract meaningful information and patterns from unstructured text data. Leveraging methods from Natural Language Processing (NLP), practitioners can perform tasks such as sentiment analysis to gauge opinion, topic modeling to identify key themes, and named entity recognition to pull out specific people or places. The ultimate goal is to transform qualitative text into quantitative, structured data, enabling analysts to uncover insights, understand trends, and make data-driven decisions from vast collections of documents, social media posts, customer reviews, and other text-based sources.

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2. Data Acquisition and Preprocessing