Identify and Extract Named Entities From Text Data

Use AI to automatically detect and classify key elements/entities from unstructured text data from any industry.

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What is Entity Extraction?

Named entity extraction also known as named entity recognition is the process of identifying and extracting named entities from unstructured text data. The entities can be people, locations, organizations, products, etc.

You can also use entity recognition to identify and organize relevant data from unstructured text.

BytesView's advanced entity extraction solution can analyze unstructured text including reviews, emails, surveys, social media conversations, etc. Identify named entities from large volumes of textual data.

Build and train custom models to identify and track entities specific to your business.

Entity Extraction Demo

Result

BytesView Entity Extraction Technical Specifications

Technical Specification of sentiment Analysis
Deployment Availability
BytesView Server
BytesView Cloud
Amazon Web Server
Plugins
Google Spreadsheet
Zendesk(Coming Soon)
Bindings
Curl,
Python,
Php,
Java,
Curl,
R,
Ruby,
C#,
Node.JS
Supported Languages
English
Industries
Specific Entity Extraction Model Developed For
Customization
Customized solution as per your need

How can your Business Benefit From a Entity Extraction tool?

Extract Insights from Customer Feedback

Online customer reviews can be a great source of insights for focused improvement. Analyze the feedback of customers and identify what they like and what they don't.

Use Named Entity recognition to identify areas of your business that need improving.

Extract Insights from Customer Feedback
Customer Support Tickets

Customer Support Tickets

As businesses grow and increase the size of their target audience support teams are often overwhelmed by the massive inflow of support tickets.

Use named entity extraction to manage support tickets and increase the response rates of your customer support teams.

Human Resources

Recruiters spend a lot of time going through tons of resumes to find the right candidate. Leverage named entity recognition and define tags to narrow down the list of candidates.

Analyze their names, skills, education, experience and more with ease and find the right candidate as per your requirements.

Human Resources

How to access BytesView Entity Extraction Tool?

Given below are the steps to access the BytesView entity extraction 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 entity extraction models, go through the video.
access gender detection

Now build and train custom Entity Extraction Models as per your need

build custom sentiment analysis

Now train custom entity extraction 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 extractor and then select entity extraction model.
  • Import your data and select which column you want to analyze if there is more than one.
  • Tag relevant text that appears to train the feature extraction 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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