Introduction

In this module, you explore how to use AI to make sense of text in documents and other written assets.

Text processing and analysis are possible through natural language processing (NLP). NLP enables machines to understand, interpret, and respond to human language. The goal of NLP is to analyze and extract meaning or structure from existing text.

Text analysis is the process of automatically examining written text to extract useful information—such as sentiment, keywords, entities, or topics. Text analysis relies on NLP to turn unstructured text into meaningful insights.

Consider some of these applications of text analysis:

• Customer Feedback Analysis: Businesses need to analyze large volumes of customer reviews, support tickets, and survey responses. With text analysis techniques, they can identify trends, detect dissatisfaction early, and improve customer experiences.

• Healthcare Text Analysis: Healthcare systems need to extract clinical information from unstructured medical documents. Text analysis techniques can help identify symptoms, medications, and diagnoses, supporting faster and more accurate decision-making.

• Financial Document Processing: Banks and financial institutions handle large volumes of contracts, loan applications, and regulatory documents. Text analysis techniques can help automatically extract key details—such as interest rates, borrower information, and compliance risks—helping reduce manual review time and improve accuracy.

• Legal Document Summarization: Law firms manage lengthy case files, court rulings, and agreements. Text analysis techniques can help summarize complex legal texts, highlight important clauses, and classify documents by topic, enabling attorneys to work more efficiently and make informed decisions faster.

Next, let's explore text analysis capabilities in Microsoft Foundry.