Text Classification 

Text classification, also known as text tagging or text categorization, categorizes text into organized groups. Using Natural Language Processing (NLP), text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.

Unstructured text such as emails, chat conversations, websites, and social media is hard to extract value from this data unless it is organized in a certain way. However, classifying the text manually is a complex and expensive process. It requires spending time and resources to sort the data or creating handcrafted rules that are difficult to maintain. Text classifiers with NLP have proven to be a great alternative to structure textual data quickly, cost-effectively, and in a scalable way.

The text classification works on the text context, so even if the document format is new, then also there are higher changes that the model would have on a larger dataset that includes all possible diverse occurrences of sample data, then you can expect better accuracy. 

Text classification is becoming an increasingly important part of businesses as it helps to get insights from data and automate business processes efficiently. 

To open Text Classification application, navigate to Smart Bot > Classification > Text Classification.

Text Classification page displays two tabs namely Dataset and Model.

• Dataset: Dataset page helps to upload datasets to the Smart Bot. This supports  ".csv"  and ".xlsx" format with two columns - Text and Class.

 The Datasets section is displayed with the following details:

• Dataset Name: Specifies the name of the dataset.
• Description: Specifies the description of the dataset.
• File Name: Specifies a name to identify the dataset.
• Created Time: Specifies the date and time of the dataset when it was created.
• Created By: Specifies the name of the user who has created the dataset.
• Actions: Use the Actions column for performing the following operations: 
  • Download:  To download the uploaded dataset.
  • Delete:  To delete the uploaded dataset.

• Model: Model Page helps to select required dataset to train a model.

Model page displays following information:

• Classifier Name: Specifies the name of the text classifier model.
• Dataset Name: Specifies the name of the selected dataset.
• Status: Specifies status of the classifier model.
  • Completed:  The model is trained successfully.
  • In Progress: The training is in progress.
  • Failed: the model failed to train.
• Description: Specifies the description of the classifier.
• Last Modified Time: Specifies the latest date and time when the text classification model was trained.
• Accuracy: Specifies the accuracy value of the trained model. The value is between 0 and 1. Higher value indicates higher confidence in the prediction. Accuracy helps choose the correct model.
• Actions: Use the Actions column for performing the following operations: 
  • Delete:  To delete the uploaded dataset.
  • Download:  To download the uploaded dataset.
  • Predict:  To predict a set of content or files.