Find the information that matters using natural language processing (NLP)

Screenshot of SAS Visual Text Analytics - visualize data with highlights

SAS Visual Text Analytics

Scale the human act of reading, organizing and extracting useful information from huge volumes of textual data.

Text analysis leader on G2

SAS Visual Text Analytics wins 'Top 50 Analytics & AI Products,' ‘Winter 2024 Leader in Text Analysis’ and ‘Winter 2024 Enterprise Leader in Text Analysis' badges from G2.

Organizations of all sizes are working smarter with SAS

Key Features

Augment human efforts to analyze unstructured text with AI using a variety of modeling approaches. Experience the combined power of natural language processing, machine learning and linguistic rules.

Large Language Model (LLM)-based classification

Capture the context and meaning of words in a text to improve accuracy compared with traditional models. In addition to general classification, the BERT-based classification can be used to do sentiment analysis​.


Text is separated into words, phrases, punctuation marks and other elements of meaning to provide the human framework a machine needs to analyze text at scale.

Trend analysis

Unsupervised machine learning groups documents based on common themes. Relevance scores calculate how well each document belongs to each topic, and a binary flag shows topic membership above a given threshold.

Information extraction

Pull out specific pieces of information or relationships between information from text using a powerful, flexible and scalable SAS proprietary programming language called language interpretation for textual information (LITI).

Hybrid modeling approaches

Build effective text models using a variety of combined capabilities, including a rich mix of linguistic rules, natural language processing, machine learning and deep learning.

Sentiment analysis

Subjective information is identified in text and labeled as positive, negative or neutral. That information is associated with an entity, and a visual depiction is provided through a sentiment indicator display.

Corpus analysis

Understand corpus structure through easily accessible output statistics to leverage natural language generation (NLG) for tasks such as data cleansing, separating out noise, sampling effectively, preparing data as input for further models (rules-based and machine learning), and strategizing modeling approaches.

Native support for 33 languages

Out-of-the-box NLP functionality enables native language analysis using dictionaries and linguistic assets created by native language experts from around the world.

Recommended Resources


Make Every Voice Heard With Natural Language Processing

Solution Brief

Natural language processing for government efficiency


SAS Blogs: Text Analytics

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