Analyze the complex emotions of your   customers followers

Compile and analyze larger volumes of textual data and identify the deep meaning behind them with emotion analysis.

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What is Emotion Analysis?

In some cases the sentiment analysis might not enough understand what the customer actually feels.

Emotion analysis is the process of identifying and analyzing the underlying emotions expressed in textual data.

Emotion analytics can extract the text data from multiple sources to analyze the subjective information and understand the emotions behind it.

BytesView's advanced machine learning techniques can help you analyze the emotions expressed by the author in a piece of text.

It can be easily done based on the types of feelings expressed in the text such as fear, anger, happiness, sadness, love, inspiring, or neutral.

Gather and analyze large volumes of text data to analyze the emotions of your followers, customers, and more.

Emotion Detection Demo

Result

Positive
Neutral
Negative

Result

Accuracy

Topic Label

Result

News

Feedback

Promotion

Query

Spam

Result

Relatedness

Result

Angry

Fear

Happy

Sad

Love

Neutral

Result

Result

Result

BytesView Emotion Analysis 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,
Spanish,
Arabic,
French,
Persian,
Japanese,
(Add Support for more than 30 language)
Industries
Specific Emotion Analysis Model Developed For
Customization
Customized solution as per your need

How can your Business Benefit From a Emotion Analysis tool?

Improve customer experience

Analyze large volumes of social media and feedback data to examine and weigh the emotions expressed in the text data. Define priorities for actions and improve user experience with our emotion analytics tool.

Improve customer experience
Monitor brand reputation

Monitor brand reputation

Compile brand related data from multiple sources and analyze it to identify the emotions of the users. Emotion analysis will help you gauge your brand’s reputation as conveyed by the users. Define alerts that can tarnish your brand's reputation.

Boast employee morale

Analyze employee feedback data to examine their happiness. Emotion analysis helps to identify early problems and resolve them before they escalate any further. Avoid losing talented individuals.

Boast employee morale

How to access BytesView Emotion Analysis Tool?

Given below are the steps to access the BytesView Emotion Analysis 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 emotion analysis models, go through the video.
access gender detection

Now build and train custom Emotion Analysis Models as per your need

build custom sentiment analysis

Now train custom emotion analysis 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 emotional 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.

Get started with BytesView

Let BytesView platform helps you solve all your text analysis needs

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