Detect and Distinguish Text Data Based on   Query Intent Topic Urgency

Use our topic labeling module to compile data from multiple sources and categorize them within seconds to improve efficiency.

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What is Topic Labeling ?

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.

It is a quick and easy method to automate business processes and provide insights for data-driven decisions.

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 Demo





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BytesView Topic Labeling Technical Specifications

Technical Specification of sentiment Analysis
Deployment Availability
BytesView Server
BytesView Cloud
Amazon Web Server
Google Spreadsheet
Zendesk(Coming Soon)
Supported Languages
Specific Topic Labeling Developed For
Customized solution as per your need

How can your business benefit from a Topic Labeling tool?

Automate Ticket Tagging

Ticket tagging is the tedious process of classifying incoming customer support tickets. If done manually it can decrease the initial response times of your customer support teams.

Use topic labeling to automate ticket tagging and increase the efficiency along with response times of your customer support teams.

Automate Ticket Tagging
Topic Tracking For Research

Topic Tracking for Research

The rapid development of the internet has increased the supply of data exponentially and finding relevant data for any research has became that much difficult in such a sea of data.

Use topic labeling to track relevant sources of information and topics.

Classification of Documents

Businesses have to process lots of of documents on a daily basis and handling them could become a tedious and very much prone to human errors.

Use topic labeling to categorize documents as per predefined categories to make storing and retrieving much more efficient.

Document classification
Product Analytics

Product Analytics

Product analytics is a complex process as there are a hundreds of decisions that have to be taken when creating them. Not knowing which ones are right and which ones are wrong?

Use topic labeling to find areas of improvement or new features to add based on insights and improve customer satisfaction.

How to access BytesView Topic Labeling tool ?

Given below are the steps to access the BytesView Topic Labeling API . You can either gather the data by yourself in an excel/CSV format or ask us to do so.

  • Gather pieces of text and documents from multiple sources
  • Compile the gathered text data in an Excel/CSV file or Google spreadsheet
  • Install the BytesView plugins on Google spreadsheet and open the add-ons tab to locate it.
  • Create a demo account or Purchase a subscription plan that suits your requirements and get an API key.
  • Paste the given API key on the BytesView plugin to activate it
  • Select the cells you want to analyze and the machine learning model you want to use. Click run to start analyzing your data.
  • For more information on topic labeling models, go through the video.
access sentiment analysis

Now build and train custom Topic Labeling models as per your need

build custom sentiment analysis

Now train custom topic labeling models with data related to your organization to further increase accuracy of the output.

  • Collect the data you want to analyze and export them as a CSV or Excel file. Use a web scraping tool or let us do it for you.
  • Go on the BytesView dashboard and click on “create a model” and chose between a classifier or an extraction model.
  • Click on classifier and then select sentiment analysis model.
  • Import your data and select which column you want to analyze if there is more than one.
  • Tag a few of the data units as Positive, Negative, or Neutral to train your model. The model will begin making its own conclusions after a few tags.
  • Name and Test your model to see how it is working.
  • Once your model is trained, you can upload the whole data set to get results.

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