Text mining engine TMS
By comprehending characteristics, meanings, and associations of large-scale internal and external non-structured data, Big data Suite‘s Text Mining Engine (TMS) performs semantic-based retrieval, information rearrangement, and multi-dimensional analysis. It offers non-structured data analysis functions to discover and add value to hidden knowledge. Accordingly, users can derive decisions such as knowledge utilization, customer management, risk management, research, and development. It extracts high-quality information from documents and information, extracts relationships, categorizes automatic information (documents), cluster automatic information (documents), summarizes automatic information (documents), and analyze intelligent unstructured data. It is an intelligent text mining engine that helps you save time while searching, analyzing, and utilizing knowledge information.
<Text mining engine block diagram>
- Built-in powerful text mining feature
- Built-in machine learning and deep learning-based high-quality morpheme analyzer
- Built-in machine learning and deep learning-based high-performance sentence structure analyzer
- Built-in machine learning and deep learning-based object name recognizer
- Built-in machine learning and deep learning-based reputation (emotion) extractor
- Built-in high-quality hybrid-type information extractor
- Best existing built-in machine learning-based big data automatic information (document) classifier
- Automatic built-in information (document) assembler
Main features and specifications
Natural language processing features
All high-precision language analyzers use machine learning and artificial neural network technology. As a result, they help optimize the domain quality through dictionaries and rules.
<natural language processing features>
Information (document) automatic categorization feature
It can classify real-time hierarchies using a pre-defined classification system (category) for large amounts of non-structured big data (information and documents). This hybrid classification feature uses both learning and rule base to classify documents.
- Automatic information (document) cluster analysis feature
- Knowledge trends analysis feature
- Related words analysis feature – topic rank
- Topic trends analysis feature
- Rapidly rising keyword analysis feature – TopN
- Spatial analysis feature