The Tatvam Rating is a sentiment rating, that is based on a 5 point scale, that is based on positive and negative words used in the text comment of an online review or other comment.
In Tatvam, each review receives an overall Tatvam rating, which is shown at the top of the review, and also each classifier that is mentioned receives its own individual Tatvam rating.
The User Rating is the rating or score that is given by the original author of the comment on the source site. An example is the star rating in a Google or Yelp review.
A keyword or key-phrase that represents a measurable topic. In the context of Tatvam, we use the term “classifiers” to identify each individual touchpoint of the customer experience for the purpose of measuring the number of mentions and customer sentiment when the classifier is mentioned.
Example classifiers include “Crowd”, “Parking Lot”, or “Plane Train”
Parent Classifier vs. Sub Classifier
In Tatvam, we create hierarchical libraries composed of keywords and phrases that we call Parent Classifiers and Sub Classifiers.
A Parent Classifier, is the overall group or category term.
A Sub Classifier is a keyword or phrase that fits under the Parent Classifier group.
An example would be:
Parent Classifier = Accessibility
Sub Classifiers = Handicapped, Ramps, Disabled, etc..
A way in which Tatvam users can apply rules to the underlying data set to show subsections of the data in the reports.
For example, if a user applied a “source filter” they could have Tatvam only show comments from a specific source like Facebook. Or, if a user applied a “classifier filter” then Tatvam would only show comments that mention a specific classifier like Crowd or Price.