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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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.
Data access, preparation & quality
Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.
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.
Data visualization
Visually explore data and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data.
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.
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.
Flexible deployment
Deploy models in batch, Hadoop, in stream and via APIs. Score code is natively threaded for distributed processing, taking maximum advantage of computing resources to reduce latency to results.
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.
Cloud native
SAS Viya's architecture is compact, cloud-native and fast. Whether you prefer to use the SAS Cloud or a public or private cloud provider, you'll be able to make the most of your cloud investment.
SAS Viya is cloud-native and cloud-agnostic
Consume SAS how you want – SAS managed or self-managed. And where you want.