Machine Learning for Topic Labeling and Text Categorization

Build custom topic labeling and text categorization modules to detect and distinguish text data based on the query, intent topic, urgency, etc. Compile data from multiple sources and categorize them within seconds to improve efficiency.

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Topic Labeling & Text Categorization Explained

Topic labeling and text categorization are a machine learning techniques that help in summarizing and distinguishing any piece of text based on its theme. It can also differentiate and catalog documents by identifying predefined keywords.

BytesView is an efficient tool that you can use to automate classification of documents with topic labeling and text categorization. Large organizations in any industry have to process a ton of documents on a daily basis. BytesView can help you segregate documents by identifying clusters of words from unstructured text data within minutes with guaranteed accuracy.

Topic Labeling and Text Categorization Technical Specifications

Technical Specification of topic labelling

BytesView’s topic labeling module is developed to analyze and interpret text data in multiple languages. You can also develop custom models for defining topics and categories to analyze data as per your requirements. BytesView can be trained to analyze text data in 30+ languages. You can also get access to the BytesView API and integrate it with your system. You can build custom topic labeling models and train them with data specific to your organization to further increase accuracy.

How to access BytesView Topic Labeling and Text Categorization Models

Given below are the steps to access the BytesView Topic labeling and classification API. You can compile the text data in an Excel/CSV file or let BytesView do it for you.

  • Gather pieces of text and documents from multiple sources
  • Compile it in an Excel/CSV file or Google Spreadsheet
  • Purchase a pricing plan that suits your requirements and install the BytesView plugins
  • After purchasing the pricing plan you will have access to our API, just put the API key for the Google Spreadsheet plugin to integrate data
  • After activating the API key, choose from our topic labeling and text categorization models or build a custom model of your own.
  • For more information, look at the video
access topic labelling

How to build and train custom topic labeling models

Get access to BytesView API and integrate it with your system. Train custom sentiment analysis models with data related to your organization to further increase accuracy of the output.

build custom topic label models
  • Upload Data: Directly upload the Excel/CSV file full of text data to the BytesView platform or use the dedicated Google Spreadsheet and Zendesk plugins to integrate data.
  • Define Tags: Define tags to help classify and categorize text data. Distinguish text data based on the emotion expressed in the text such as fear, anger, happiness, love, sadness, and more.
  • Tag & Train: Select and tag relevant text that appears to train the model. It will help the topic labeling model more be efficient in classifying text data with maximum accuracy.
  • Evaluate and Improve: Test and evaluate the accuracy of the trained topic labeling model. Tag more data if needed to increase the accuracy and efficiency of the model.
  • Put your custom emotion analytics to work: After you are done training and testing the model, use it to analyze complex text data. Upload textual data directly to BytesView or use the plugins to integrate the data. You can also integrate the BytesView API with your system.

Applications/Use Cases of Topic Labeling and Text Categorization

Bio-informatics

Topic labelling text analytics models can help biologists discover latent patterns in massive biological data.

Opinion and Meeting Summarization

Transcripts of multi-person meetings are highly complex and diverse. Use topic labeling and text categorization to narrow down the focus on topically relevant conversations.

Document classification

Businesses have to process a ton of documents on a daily basis. Use topic labeling to categorize documents as per predefined categories.

Topic Tracking for Research

The rapid development of the internet has increased the supply of data exponentially. Use topic labeling and text categorization to track relevant sources of information and topics.

Business Intelligence

Use topic labeling and text categorization to identify recurring themes or topics in large sets of data. Analyze and dissect valuable insights to plan effective strategies.

Product Analytics

Product managers always need insights to make their product more appealing to the customers. Use topic labeling to find areas of improvement or new features to add to fulfil the customers needs.