Text Classification Provides Meaning from Unstructured Content
Text classification software provides the data you need to create actionable insights.
Because unstructured content is text-based, it is easily converted using optical character recognition software to perform text analytics. As organizations digitally transform, processing unstructured data is more crucial than ever.
Transform your data pipeline with a single-source text classification software with built-in optical character recognition, sentiment analysis, part-of-speech tagging, named entity tagging, natural language processing, and machine learning.
Build classification models for document recognition and data extraction with easy to use machine learning models.
Trust extracted information with transparent training data displayed in easy to understand confidence thresholds and weightings.
Discover a Powerful Real-World Text Classification Application:
Time was running out for a leading pharmaceutical company. After a recent acquisition, a massive data onboarding project threatened to stall the release of newly acquired products to market.
Newly acquired drugs would be pulled from the market if regulatory commitments weren’t kept, so they needed a rapid classification solution. And manually sifting through the data was not an option.
Time was of the essence because not only would beneficial drugs be unavailable to patients – they risked losing thousands of dollars in revenue – daily….
Text classification, powered by Grooper, to the rescue! After implementing Grooper, they automated a process to find the drugs that required immediate attention for regulatory approval.
Because Grooper ingests all types of data, the process was repeatable and scalable for future acquisitions.
Text Classification with Grooper Helped:
Grooper’s classification is solving problems like this every day. And the best part: you don’t need complicated public domain code like Python to get the results you need.
Grooper’s Text Classification Algorithm
Build and test models and solve real problems without the limitations of other tools like TensorFlow, NumPy, or Watson Natural Language Classifier. Choose Grooper for accurate and dependable document classification.
Grooper uses the term frequency inverse document frequency (TFIDF) machine learning text classification algorithm to quickly classify text and documents. TFIDF works by identifying words (or inputs) that are unique (or more common) in a particular type of document compared to the document set overall.
It’s a deceptively simple way of classifying documents, and it generally does so in a similar way to how humans do it: by looking at the individual words on the document.
Our TFIDF implementation is used for extraction, separation, and classification, and provides an inspection of feature weightings, which helps you directly and completely understand how it’s making decisions.
How Text Classification is Saving Millions
Hundreds of organizations are saving money and time every day by using Grooper’s classification. In another real-world example, discover how one company:
Download our case spotlight to learn more!
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