Data Categorization with VEDO

Add Business Context To Social Data

See how VEDO data categorization can turn your social data into actionable business insight.PlayWatch Video

  • Automatically categorize social data based on its meaning, helping you to transform it into useful data for your app. Answer questions such as, "Where are my customers in the buying cycle?" "How important is this message?", or "Is this user interested in my program?"


    By adding hierarchical tagging and scoring to posts, blogs and tweets, you can increase the relevance of the data, making it more actionable and ready for deeper analysis.

Programmable Intelligence provides three powerful approaches to help extract meaning and context within each post, tweet or comment:

Library of VEDO classifiers

  •  Get a jump start using our built-in data science or find inspiration in our our collection of best-practice models.
  •  Use the classifiers out of the box or as a starting point to identify potential spam, leverage machine learning to categorize social posts into complaints, compliments or requests for help, or score affinity towards a product or topic.

Custom Taxonomies

  •  Analysts and developers can easily create custom rules to classify data based on any combination of criteria found within the content, text-patterns or metadata.
  •  Create rules to match conversations to your business taxonomy, such as to your products hierarchy or organizational structure and receive data classified exactly to your business needs.

Machine Learning

  •  Apply machine-learning models to train a classifier, then run it in VEDO. Create an intent-classifier to identify purchase signals from social data.
  •  Train and build models based on Bayesian classification, logistic regression and support vector machines. Extract deeper meaning and increasing accuracy for your analytics.

Learn more about VEDO

Discover more about DataSift VEDO, its benefits to your business in making Social data actionable and how to practically use if various features.