Rubitext simplifies Text Analytics with a set of Linguistic, Statistical, and ML techniques for Word Frequency Analysis, Pattern Recognition, Tagging/Annotation, Entity Extraction, Link & Association Analysis, Sentiment detection. It also offers features for Clustering, and various Classification techniques such as intent recommended actions.


  • High-performance text parsing includes automated part-of-speech and noun group detection, entity and multiword term identification, stemming, and synonym detection
  • Term and frequency weighting can be configured from default settings
  • Graphical and tabular output assesses terms and their distributions within a collection
  • Predictive algorithms for text analysis with built-in feature extraction (vectorization)
  • High-performance, the target-based weighting, is available for more accurate categorical target estimates
  • Large-scale text data scoring enabled
  • It enables you to describe and predict a target variable based on specific terms
  • Results and graphs are interactively linked for easy exploration
  • Clarify which terms are more meaningful to one another
  • Visually assess emerging or declining terms over time, including how terms co-relation analysis
  • When the target variable is time, the Profile node illustrates the trends of terms over the selected time