Whether your application supports customer satisfaction analysis, root cause determination, or churn predication, your application needs to classify large body of text data into correct set of groups. An incorrect classification could potentially lead to wrong conclusions, mis-allocation of resources, and delay in resolution of problems.
Manual approaches to classification of data can be expensive, error prone, and unsuitable for a large amount of data. Automatic classification systems are also prone to error and poor quality. For example, classification can be affected by uncertain labeling semantics and ambiguity in text.
IBM Tool for Interactive Text Classification and Labeling bridges this gap and provides a comprehensive interactive interface for:
· correcting and refining automatic text classification models through human feedback
· continuously inspecting and validating pre-classified data in order to ensure
· consistency building "good" training data from scratch.
IBM Tool for Interactive Text Classification and Labeling will help you in the process of classifying data.
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