Emotion Analytics with Machine Learning

Analyze the complex emotions expressed in any piece of text. Compile and analyze larger volumes of textual data and identify the deep meaning behind them with emotion analysis.

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Emotion Analysis Explained

Emotion analysis is the process of identifying and analyzing the 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. Emotion detection and classification 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 Analytics Technical Specifications

Technical Specification of emotion analytics

BytesView's emotion analysis tool is specifically designed to analyze large volumes of text data and transform it into actionable insights. It is capable of analyzing unstructured textual data in multiple dialects and formats. Integrate the BytesView API with your system to streamline data gathering, or build custom emotion analytics models trained with your organization's data for accurate emotion detection.

How to Access BytesView Emotion Analysis

Given below are the steps to access the BytesView emotion analytics API. You can gather the data by yourself in an Excel/CSV file, or you can use the Bytes View to do it for you.

  • Gather textual data from multiple sources including review,feedback, opinions, support data, social media, etc for the emotion analysis.
  • Compile the accumulated text data into an Excel/CSV file.
  • Go to BytesView, purchase a pricing plan, and install the dedicated plugins.
  • After you purchase the plan, you will get access to the BytesView API. Put the API key in the Google spreadsheet plugin to integrate the text data.
  • After activating the API key, choose an emotion analysis model or build a custom trained model to analyze data.
  • For any more information on emotion analysis, go through the video.
access emotion analytics

How to build and train custom emotion analytics models ?

Get access to BytesView API and integrate it with your system. Train custom emotion analytics models with data specific to your organization to increase accuracy of the analyzed data.

build custom emotion analytics
  • 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:s Select and tag relevant text that appears to train the model. It will help the model more be efficient in classifying text data with maximum accuracy.
  • Evaluate and Improve: Test and evaluate the accuracy of the trained emotion analysis model. Tag more data if needed to increase the accuracy and efficiency of the emotion analytics 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 Emotion Analysis

Analyze social media and feedback data

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.

Brand reputation monitoring

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.

Measure happiness of your employees

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. Remember, happier employees provide better results.