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Emotion Analytics Market Size 2024-2028

The emotion analytics market size is forecast to increase by USD 4.44 billion, at a CAGR of 18.2% between 2023 and 2028.

The market is experiencing significant growth due to the increasing demand for data-driven customer behavior management in various sectors, including IT and telecommunications. Emotion recognition technology, which includes tone of voice analysis and correlation engines, is increasingly being used in public safety and contact centers to enhance situational awareness and improve response times. In the automotive industry, emotion analytics is being utilized to provide personalized user experiences and optimize sales performance. Advanced audio mining algorithms and artificial intelligence are being employed to extract insights from labeled emotion data obtained from various sources, including wearable gadgets and audio recordings. The accuracy and reliability of emotion analytics solutions are key drivers of market growth, making them valuable tools for businesses seeking to improve customer engagement and satisfaction.

What will be the Size of the Market During the Forecast Period?

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The market is witnessing significant growth as businesses increasingly recognize the value of understanding and responding to human emotions. Emotion analytics is a branch of artificial intelligence that focuses on the identification, analysis, and interpretation of emotions from various data sources. This market is poised to revolutionize industries by providing valuable insights into customer and employee behavior, public safety, and fraud detection. Behavioral analytics is a crucial application of emotion analytics. By analyzing voice tone, facial expressions, and text data, businesses can gain a deeper understanding of their customers' emotions and intentions. This information can be used to optimize sales performance, improve customer experience, and enhance brand reputation management. Another key application of emotion analytics is in risk management. By analyzing emotions in real-time, businesses can identify potential threats and take preventative measures. For instance, in public safety analytics, emotion analytics can be used to detect anomalous behavior and alert authorities.
In fraud detection, it can help identify suspicious transactions based on the emotional tone of the communication. Emotion analytics also plays a vital role in human-computer interaction. Conversational AI, a subset of emotion analytics, enables machines to understand and respond to human emotions. This technology can be used to improve customer service, automate contact centers, and enhance the overall customer experience. predictive analytics is another area where emotion analytics is making a significant impact. By analyzing historical data and identifying patterns, emotion analytics can predict customer churn, agent performance, and product perception. This information can help businesses take proactive measures to retain customers and improve employee performance. Cloud-based technology is driving the adoption of emotion analytics in various industries. Emotion analytics software, which can be integrated with existing systems, offers real-time insights and easy scalability. Sentiment analysis tools, a popular application of emotion analytics, help businesses measure customer satisfaction and monitor brand reputation.

How is this market segmented and which is the largest segment?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Customer experience management
  Sales and marketing management
  Competitive intelligence
  Public safety and law enforcement
  Others


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

By Application Insights

The customer experience management segment is estimated to witness significant growth during the forecast period.

In today's business landscape, understanding user experience and emotions plays a pivotal role in gaining customer satisfaction and loyalty. Emotion analytics, a growing field, enables companies to diagnose and respond to customers' emotions using advanced machine learning algorithms. This data is collected through various sources such as virtual reality (VR), wearables, and user feedback. Emotion recognition technology, which includes facial recognition and haptic touch, is a significant component of emotion analytics. By analyzing customers' emotional responses, businesses can tailor their interactions, creating a more engaging experience. For instance, this technology can be used to gauge viewers

URDU-Dataset

1. General information

URDU dataset contains emotional utterances of Urdu speech gathered from Urdu talk shows. It contains 400 utterances of four basic emotions: Angry, Happy, Neutral, and Emotion. There are 38 speakers (27 male and 11 female). This data is created from YouTube. Speakers are selected randomly.

The similar version of dataset is uploaded to DagsHub: URDU-Dataset, enabling you to preview the dataset before downloading it.

2. Structure

Nomenclature followed while naming the files in the dataset is to provide information about the speaker, gender, number of the file for that speaker and overall numbering of the file in particular emotion. Files are named as follows:

General Name: SGX_FXX_EYY

For more details about dataset, please refer the complete paper "Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages". https://arxiv.org/pdf/1812.10411.pdf

2.1 Audio files:

• [21M] Angry - of approximately 242 seconds of "clean" speech in .wav format (pushed to DagsHub)
• [21M] Happy - of approximately 244 seconds of "clean" speech .wav format (pushed to DagsHub)
• [21M] Neutral - of approximately 244 seconds of "clean" speech .wav format (pushed to DagsHub)
• [21M] Sad - of approximately 244 seconds of "clean" speech .wav format (pushed to DagsHub)

2.2 Organization of the Emotions dataset

The dataset is small (88MB) and simple to navigate as it has 4 folders based on emotions. Each folder contains 100 .wav audio files containing the emotions of Urdu speakers. The audio file range from 2~3 second of audio taken from a various video uploaded on YouTube. The following ASCII diagram depicts the directory structure.