SAS Visual Text Analytics
Scale the human act of reading, organizing and extracting useful information from huge volumes of textual data.
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Calibrate your LLM to improve outcomes with fit-to-task models
Manage toxicity, bias and bad actors to ensure trustworthy results. Improve data curation and minimize hallucinations by providing more relevant data to the LLM. Prevent private and sensitive data from entering the model by adding a layer of traceability.
Text analysis leader on G2
SAS Visual Text Analytics wins ‘Summer 2024 Leader in Text Analysis’ and ‘Summer 2024 Enterprise Leader in Text Analysis' badges from G2.
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
Use linguistic models to curate the best data for LLM fine-tuning and RAG. Augment LLM content moderation to detect toxicity and bias and prevent private data leakage to improve LLM outcomes without modifying or impacting the LLM.
LLM calibration
Use linguistic models to curate the best data for LLM fine-tuning and RAG. Augment LLM content moderation to detect toxicity and bias and prevent private data leakage to improve LLM outcomes without modifying or impacting the LLM.
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.
Parsing
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.
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.
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