Introduction

Within artificial intelligence (AI), text analysis is a subset of natural language processing (NLP) that enables machines to extract meaning, structure, and insights from unstructured text. Organizations use text analysis to transform customer feedback, support tickets, contracts, and social media posts into actionable intelligence.

Techniques to process and analyze text evolved over many years, from simple statistical calculations based on term-frequency to vector-based language models that encapsulate semantic meaning. Some common use cases for text analysis include:

• Key term extraction: Identifying important words and phrases in text, to help determine the topics and themes it discusses.
• Entity detection: Identifying named entities mentioned in text; for example, places, people, dates, and organizations.
• Text classification: Categorizing text documents based on their contents. For example, filtering email as spam or not spam.
• Sentiment analysis: A particular form of text classification that predicts the sentiment of text - for example, categorizing social media posts as positive, neutral, or negative.
• Text summarization: Reducing the volume of text while retaining its salient points. For example, generating a short one-paragraph summary from a multi-page document.

Text analysis is challenging because language is complex, and computers find it hard to understand. Ultimately, all text analysis techniques are based on the requirement to extract meaning from natural language text.