Many organisations are sitting on a repository of text documents. However, organisations are unable to quickly embark on their Machine Learning journey because these text documents are not labelled. Companies have to invest significant resources to manually label their text documents. Even so, labelling may not be consistent across all documents, as different labellers could be involved.
This tool will speed up your labelling efforts and ensure labelling uniformity. It will assign meaningful tags or categories to short texts according to their content so that recurring and dominant themes can be identified.
Helps companies kick-start supervised machine learning
Reduce manpower efforts and time required to label
Reduces subjectivity and allows same labelling process to be applied across text documents
Process is easily repeatable if user wants to identify new labels
Collate all documents into one file
Automated pre-processing of file
Create a label dictionary (list of key words that is relevant to specific labels)
Automatically train the auto labeller and apply the labeller!
Type of feedback (e.g. speed of delivery, after-sales services, product defects)
Grouping incidents
Business functions (e.g. marketing, communications, sales, human resource, etc)
