📄 Description The Emotion Detection Dataset consists of short text samples labeled with human emotions such as joy, sadness, fear, and neutral. These texts mimic real-world, informal communication typically found on social media platforms like Twitter, chat apps, or online forums.

Each data point is a pair of:

Text: A short sentence or phrase

Emotion: The dominant emotion expressed in the text

This dataset is curated for use in natural language processing (NLP) tasks, especially emotion classification, sentiment analysis, and emotion-aware AI applications.

🎯 Purpose The goal of this dataset is to help train and evaluate models that can automatically detect human emotions in text. It can be used for:

Building emotion classifiers using machine learning

Fine-tuning transformer models (e.g., BERT, RoBERTa)

Sentiment analysis in real-world scenarios

Chatbot emotion understanding

Research in affective computing and social NLP

Overview:

The Emotion Classification dataset is curated to support research and practical applications in natural language processing (NLP) and emotion detection. It includes a variety of text samples, each categorized by the specific emotion expressed. Emotions range from positive states like joy to negative ones like fear and anger, offering a wide scope for analysis.

Content: Format: CSV Labels: ['anger', 'joy', 'fear']

Use Cases:

• Sentiment analysis and emotion detection
• Building emotion-aware NLP models
• Analyzing customer feedback
• Monitoring sentiment on social media platforms
• Training chatbots and virtual assistants to understand emotions

Dataset Card for "emotion"

  Dataset Summary

Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure





  Data Instances

An example looks as follows. { "text": "im feeling quite sad and sorry for myself but… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.

Emotions in Literature

Description Literature sentences from Project Gutenberg. 38 emotion labels (+neutral examples). Semi-Supervised dataset.

Article

Detecting Fine-Grained Emotions in Literature

Please cite:

@Article{app13137502, AUTHOR = {Rei, Luis and Mladenić, Dunja}, TITLE = {Detecting Fine-Grained Emotions in Literature}, JOURNAL = {Applied Sciences}, VOLUME = {13}, YEAR = {2023}, NUMBER = {13}, ARTICLE-NUMBER = {7502}, URL = {https://www.mdpi.com/2076-3417/13/13/7502}, ISSN = {2076-3417}, DOI = {10.3390/app13137502} }

Abstract

Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model's performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.

Labels

• admiration: finds something admirable, impressive or worthy of respect

• amusement: finds something funny, entertaining or amusing

• anger: is angry, furious, or strongly displeased; displays ire, rage, or wrath

• annoyance: is annoyed or irritated

• approval: expresses a favorable opinion, approves, endorses or agrees with something or someone

• boredom: feels bored, uninterested, monotony, tedium

• calmness: is calm, serene, free from agitation or disturbance, experiences emotional tranquility

• caring: cares about the well-being of someone else, feels sympathy, compassion, affectionate concern towards someone, displays kindness or generosity

• courage: feels courage or the ability to do something that frightens one, displays fearlessness or bravery

• curiosity: is interested, curious, or has strong desire to learn something

• desire: has a desire or ambition, wants something, wishes for something to happen

• despair: feels despair, helpless, powerless, loss or absence of hope, desperation, despondency

• disappointment: feels sadness or displeasure caused by the non-fulfillment of hopes or expectations, being or let down, expresses regret due to the unfavorable outcome of a decision

• disapproval: expresses an unfavorable opinion, disagrees or disapproves of something or someone

• disgust: feels disgust, revulsion, finds something or someone unpleasant, offensive or hateful

• doubt: has doubt or is uncertain about something, bewildered, confused, or shows lack of understanding

• embarrassment: feels embarrassed, awkward, self-conscious, shame, or humiliation

• envy: is covetous, feels envy or jealousy; begrudges or resents someone for their achievements, possessions, or qualities

• excitement: feels excitement or great enthusiasm and eagerness

• faith: expresses religious faith, has a strong belief in the doctrines of a religion, or trust in god

• fear: is afraid or scared due to a threat, danger, or harm

• frustration: feels frustrated: upset or annoyed because of inability to change or achieve something

• gratitude: is thankful or grateful for something

• greed: is greedy, rapacious, avaricious, or has selfish desire to acquire or possess more than what one needs

• grief: feels grief or intense sorrow, or grieves for someone who has died

• guilt: feels guilt, remorse, or regret to have committed wrong or failed in an obligation

• indifference: is uncaring, unsympathetic, uncharitable, or callous, shows indifference, lack of concern, coldness towards someone

• joy: is happy, feels joy, great pleasure, elation, satisfaction, contentment, or delight

• love: feels love, strong affection, passion, or deep romantic attachment for someone

• nervousness: feels nervous, anxious, worried, uneasy, apprehensive, stressed, troubled or tense

• nostalgia: feels nostalgia, longing or wistful affection for the past, something lost, or for a period in one's life, feels homesickness, a longing for one's home, city, or country while being away; longing for a familiar place

• optimism: feels optimism or hope, is hopeful or confident about the future, that something good may happen, or the success of something - pain: feels physical pain or is experiences physical suffering

• pride: is proud, feels pride from one's own achievements, self-fulfillment, or from the achievements of those with whom one is closely associated, or from qualities or possessions that are widely admired

• relief: feels relaxed, relief from tension or anxiety

• sadness: feels sadness, sorrow, unhappiness, depression, dejection

• surprise: is surprised, astonished or shocked by something unexpected

• trust: trusts or has confidence in someone, or believes that someone is good, honest, or reliable

Dataset

EmoLit (Zenodo)

Code

EmoLit Train (Github)

Models

• LARGE

• BASE

• DISTILL

Dataset

This dataset was created by CHANDRU G

Contents

The most popular news portal's Facebook pages such as Prothom Alo, BBC Bangla, BD News 24, Bangla Tribune, Kaler Kantho, Daily Jugantor are picked to build the dataset. Following a manual collection of posts, a total of 130 posts for 11 news topics were obtained and converted into a CSV file. The dataset is annotated in Ekman's seven universal emotions and they are collected using a self-developed scraper algorithm.

In this dataset, there are Bangla sentences manually labeled with three emotions- Happy, Sad, and Angry.

😊😢😡 Tanaos Emotion Detection Training Dataset

This dataset was created synthetically by Tanaos with the Artifex Python library. The dataset is designed to train and evaluate emotion detection systems — models that classify the main emotion expressed in text as one of eight possible categories: joy, anger, fear, sadness, surprise, disgust, excitement, or neutral. It can be used to build emotion detection models for various applications, such as customer feedback analysis… See the full description on the dataset page: https://huggingface.co/datasets/tanaos/synthetic-emotion-detection-dataset-v1.

This dataset advances the "Emotions dataset for NLP" by Praveen (https://www.kaggle.com/datasets/praveengovi/emotions-dataset-for-nlp/data).

This dataset contains an additional neutral emotion label, which was obtained from the "Emotion Detection from Text" by Pashupati Gupta (https://www.kaggle.com/datasets/pashupatigupta/emotion-detection-from-text).

Emotions in Literature

Description Literature sentences from Project Gutenberg. 38 emotion labels (+neutral examples). Semi-Supervised dataset.

Article

Detecting Fine-Grained Emotions in Literature

Please cite:

@Article{app13137502, AUTHOR = {Rei, Luis and Mladenić, Dunja}, TITLE = {Detecting Fine-Grained Emotions in Literature}, JOURNAL = {Applied Sciences}, VOLUME = {13}, YEAR = {2023}, NUMBER = {13}, ARTICLE-NUMBER = {7502}, URL = {https://www.mdpi.com/2076-3417/13/13/7502}, ISSN = {2076-3417}, DOI = {10.3390/app13137502} }

Abstract

Emotion detection in text is a fundamental aspect of affective computing and is closely linked to natural language processing. Its applications span various domains, from interactive chatbots to marketing and customer service. This research specifically focuses on its significance in literature analysis and understanding. To facilitate this, we present a novel approach that involves creating a multi-label fine-grained emotion detection dataset, derived from literary sources. Our methodology employs a simple yet effective semi-supervised technique. We leverage textual entailment classification to perform emotion-specific weak-labeling, selecting examples with the highest and lowest scores from a large corpus. Utilizing these emotion-specific datasets, we train binary pseudo-labeling classifiers for each individual emotion. By applying this process to the selected examples, we construct a multi-label dataset. Using this dataset, we train models and evaluate their performance within a traditional supervised setting. Our model achieves an F1 score of 0.59 on our labeled gold set, showcasing its ability to effectively detect fine-grained emotions. Furthermore, we conduct evaluations of the model's performance in zero- and few-shot transfer scenarios using benchmark datasets. Notably, our results indicate that the knowledge learned from our dataset exhibits transferability across diverse data domains, demonstrating its potential for broader applications beyond emotion detection in literature. Our contribution thus includes a multi-label fine-grained emotion detection dataset built from literature, the semi-supervised approach used to create it, as well as the models trained on it. This work provides a solid foundation for advancing emotion detection techniques and their utilization in various scenarios, especially within the cultural heritage analysis.

Labels

• admiration: finds something admirable, impressive or worthy of respect

• amusement: finds something funny, entertaining or amusing

• anger: is angry, furious, or strongly displeased; displays ire, rage, or wrath

• annoyance: is annoyed or irritated

• approval: expresses a favorable opinion, approves, endorses or agrees with something or someone

• boredom: feels bored, uninterested, monotony, tedium

• calmness: is calm, serene, free from agitation or disturbance, experiences emotional tranquility

• caring: cares about the well-being of someone else, feels sympathy, compassion, affectionate concern towards someone, displays kindness or generosity

• courage: feels courage or the ability to do something that frightens one, displays fearlessness or bravery

• curiosity: is interested, curious, or has strong desire to learn something

• desire: has a desire or ambition, wants something, wishes for something to happen

• despair: feels despair, helpless, powerless, loss or absence of hope, desperation, despondency

• disappointment: feels sadness or displeasure caused by the non-fulfillment of hopes or expectations, being or let down, expresses regret due to the unfavorable outcome of a decision

• disapproval: expresses an unfavorable opinion, disagrees or disapproves of something or someone

• disgust: feels disgust, revulsion, finds something or someone unpleasant, offensive or hateful

• doubt: has doubt or is uncertain about something, bewildered, confused, or shows lack of understanding

• embarrassment: feels embarrassed, awkward, self-conscious, shame, or humiliation

• envy: is covetous, feels envy or jealousy; begrudges or resents someone for their achievements, possessions, or qualities

• excitement: feels excitement or great enthusiasm and eagerness

• faith: expresses religious faith, has a strong belief in the doctrines of a religion, or trust in god

• fear: is afraid or scared due to a threat, danger, or harm

• frustration: feels frustrated: upset or annoyed because of inability to change or achieve something

• gratitude: is thankful or grateful for something

• greed: is greedy, rapacious, avaricious, or has selfish desire to acquire or possess more than what one needs

• grief: feels grief or intense sorrow, or grieves for someone who has died

• guilt: feels guilt, remorse, or regret to have committed wrong or failed in an obligation

• indifference: is uncaring, unsympathetic, uncharitable, or callous, shows indifference, lack of concern, coldness towards someone

• joy: is happy, feels joy, great pleasure, elation, satisfaction, contentment, or delight

• love: feels love, strong affection, passion, or deep romantic attachment for someone

• nervousness: feels nervous, anxious, worried, uneasy, apprehensive, stressed, troubled or tense

• nostalgia: feels nostalgia, longing or wistful affection for the past, something lost, or for a period in one's life, feels homesickness, a longing for one's home, city, or country while being away; longing for a familiar place

• optimism: feels optimism or hope, is hopeful or confident about the future, that something good may happen, or the success of something - pain: feels physical pain or is experiences physical suffering

• pride: is proud, feels pride from one's own achievements, self-fulfillment, or from the achievements of those with whom one is closely associated, or from qualities or possessions that are widely admired

• relief: feels relaxed, relief from tension or anxiety

• sadness: feels sadness, sorrow, unhappiness, depression, dejection

• surprise: is surprised, astonished or shocked by something unexpected

• trust: trusts or has confidence in someone, or believes that someone is good, honest, or reliable

Dataset

EmoLit (Zenodo)

Code

EmoLit Train (Github)

Models

• LARGE

• BASE

• DISTILL

Dataset for the Shared Task: Bridging the Gap in Text-Based Emotion Detection – SemEval 2025, Task 11

Hi, I’m Seniru Epasinghe 👋

I’m an AI enthusiast, working on machine learning projects and open-source contributions.
I enjoy exploring AI pipelines, natural language processing, and building tools that make development easier.

Connect with me:

Hugging Face
Medium
LinkedIn
GitHub

Multimodal Emotion Recognition Dataset (Processed from MELD)

This dataset is a preprocessed and balanced version of the MELD Dataset, designed for multimodal emotion recognition research.
It combines text, audio, and video modalities, each represented by a set of emotion probability distributions predicted by pretrained or custom-trained models.

Overview

Each row in the dataset corresponds to a single utterance, along with emotion label, file name, and predicted emotion probabilities per modality.

Example Entry

Column Description:

• Utterance — spoken text in the conversation.
• Emotion — gold-standard emotion label.
• File_Name — corresponding video file (utterance-level).
• MultiModel Predictions — JSON object containing model-predicted emotion probability vectors for each modality.

Modality Emotion Extraction

Each modality’s emotion vector was generated independently using specialized models:

Format and Usage

• File format: CSV
• Recommended columns:
  • Utterance
  • Emotion
  • File_Name
  • Final_Emotion (JSON: { "video": [...], "audio": [...], "text": [...] })

This dataset is ideal for: - Fusion model training - Fine-tuning multimodal emotion models - Benchmarking emotion fusion strategies - Ablation studies on modality importance

Citation

References for the original MELD Dataset - S. Poria, D. Hazarika, N. Majumder, G. Naik, R. Mihalcea, E. Cambria. MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversation (2018). - Chen, S.Y., Hsu, C.C., Kuo, C.C. and Ku, L.W. EmotionLines: An Emotion Corpus of Multi-Party Conversations. arXiv preprint arXiv:1802.08379 (2018).

License & Acknowledgments

This dataset is a derivative work of MELD, used here for research and educational purposes.
All credit for the original dataset goes to the MELD authors and contributors.

Dataset Summary

The GoEmotions Cleaned dataset is a refined version of the original Google GoEmotions dataset. It has been cleaned, simplified, and reformatted for use in text classification tasks such as emotion detection, sentiment analysis, and multi-label emotion prediction. This version retains only two essential columns — text and label — making it ideal for model fine-tuning and experimentation with Transformer-based architectures.

  Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/Keyurjotaniya007/go-emotions-cleaned.

The EQN framework is a micro-emotion annotation and detection system that realizes the automatic micro-emotion annotation of text with energy level scores for the first time. The text emotion datasets it annotates are no longer simple single-label or multi-label, but macro-emotions and micro-emotions with continuous values ​​of emotion intensity. The labeling of emotion datasets has changed from discrete to continuous. It plays an important role in the subtle research of emotions in fields such as emotional computing, human-computer alignment, humanoid robots, and psychology.This is the experimental result of the EQN micro-emotion detection and annotation framework we proposed, the train.csv of the Goemotions dataset with micro-emotion labels with energy level intensity valuesand the model trained on the Goemotions dataset based on the BERT model. Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. For the specific implementation method, please refer to our paperNote:1. gotrainadd.csv: Goemotions dataset with additional annotation (micro-emotion labels with energy level intensity values(0-10)).2. 28pd.py: Micro-emotion detection and annotation code based on pytorch.3. 55770-1.pth: Model trained on the Goemotions dataset based on the BERT model (emotion energy level intensity is a value between 0-1).4. Goemotions dataset: Data and code available at https://github.com/google-research/google-research/tree/master/goemotionsThe experimental environment of this project.GPU：NVIDIA GeForce RTX 3090 GPUBert-base-cased pre-trained model: https://huggingface.co/google-bert/bert-base-casedpython=3.7，pytorch=1.9.0，cudatoolkit=11.3.1，cudnn=8.9.7.29.Instructions for use:1. Refer to our usage environment instructions and install the operating environment.2. Download our EQN-model.3. Change the loading model name in 28pd.py to the actual name of the downloaded EQN-model.4. Create a directory named "28pd" to place the .csv format data files to be labeled or predicted.

The Bengali Annotated Emotion Dataset (BAED) draws its content from Bengali novels which form the basis for its emotional expression and cultural element identification capabilities that other NLP datasets do not possess. The system divides into seven categories which enable complete multi-class affective state classification through anger, disgust, fear, joy, sadness, surprise and anticipation. Drawing from both dialogue and narration, BAED offers a rich basis for studying human emotions in text.The system has multiple uses in computational linguistics and sentiment and stylistic analysis and cultural and psychological research and dialogue-level emotion detection and benchmarking emotion classification models with potential future applications in newspaper and social media and conversational data.

The dataset is organized into seven balanced classes, each representing a different emotion domain:

Anger:500 data Disgust:500 data Fear:500 data Joy:500 data Sadness:500 data Surprise:500 data Anticipation:500 data

Total Number of Data: 3,500 Language: Bangla File Format: CSV file

Speech Emotion Recognition Voice Dataset

The audio dataset consists of a collection of texts spoken with four distinct emotions. These texts are spoken in English and represent four different emotional states: euphoria, joy, sadness and surprise. Each audio clip captures the tone, intonation, and nuances of speech as individuals convey their emotions through their voice.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

The dataset includes a diverse range of speakers, ensuring variability in age, gender, and cultural backgrounds, allowing for a more comprehensive representation of the emotional spectrum.

The dataset is labeled and organized based on the emotion expressed in each audio sample, making it a valuable resource for emotion recognition and analysis. Researchers and developers can utilize this dataset to train and evaluate machine learning models and algorithms, aiming to accurately recognize and classify emotions in speech.

The audio dataset also provides an opportunity for various applications:

• sentiment analysis
• automatic emotion detection
• emotional speech synthesis.
• voice assistants
• customer service
• mental health analysis
• entertainment industries

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F618942%2F94459eaec038a29404e3a7afef3fb88c%2Faudio0.png?generation=1689235055331390&alt=media" alt="">

Content

• files: includes folders corresponding to people and containing text spoken in English in 4 different manners: euphoric, joyfully, sad and surprised
• .csv file: contains information about people in the dataset

File with the extension .csv

includes the following information for each set of media files:

• set_id: link to the set of audio files,
• text: text spoken in the audio set,
• gender: gender of the person,
• age: age of the person,
• country: country of the person

Audio with emotions might be collected in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

keywords: voice assistants speech processing, ser, audio signals, artificial intelligence, ai, emotion recognition, emotional data, recognize human emotions, conversational analytics, speech features, computational paralinguistics, dataset, speech-to-text dataset, speech recognition dataset, audio dataset, automatic speech recognition dataset, voice dataset, human speech recognition, audio recording of human voices, speech recognition russian, stt/asr, deep speech, deep learning, machine learning, human-labeled sound, sound vocabulary, voice dataset, audio data

The Europe AI-powered Emotion Analytics Platform Market would witness market growth of 17.4% CAGR during the forecast period (2025-2032). The Germany market dominated the Europe AI-powered Emotion Analytics Platform Market by Country in 2024, and would continue to be a dominant market till 2032; th

GoEmotions Ekman Emotions Dataset

  Dataset Description

This dataset contains 10,000 text samples from Reddit comments mapped to the 7 basic Ekman emotions. It's derived from the original GoEmotions dataset and processed specifically for emotion classification research using Paul Ekman's fundamental emotion model.

  Supported Tasks

Text Classification: Multi-class emotion classification Sentiment Analysis: Fine-grained emotion detection Psychology Research:… See the full description on the dataset page: https://huggingface.co/datasets/Frankhihi/goemotion-ekman-emotions.

Dataset Card for EmotionAnalysisFinal

EmotionAnalysisFinal is the official dataset for SemEval-2025 Task 11, Track C: Cross-lingual Emotion Detection in Social Media Text. This dataset comprises multilingual social media posts annotated for six basic emotions: anger, disgust, fear, joy, sadness, and surprise. The annotation schema is multi-label. Each language-specific configuration (subset) contains validation and test splits.

Split Original Source Notes

validation dev… See the full description on the dataset page: https://huggingface.co/datasets/llama-lang-adapt/EmotionAnalysis.

Snapshot img

Emotion Recognition and Sentiment Analysis Software Market Size 2024-2028

The emotion recognition and sentiment analysis software market size is forecast to increase by USD 797.17 million at a CAGR of 14.15% between 2023 and 2028.

The market is experiencing significant growth, driven by the increasing popularity of wearable devices and the adoption of real-time sensing analysis. These technologies enable more accurate and timely emotion recognition, providing valuable insights for various applications, including healthcare, marketing, and customer service. However, the market faces challenges, most notably the issue of low-quality video content hampering emotional interpretation. Regulatory hurdles also impact adoption, as organizations navigate complex data privacy and security regulations.
To capitalize on market opportunities and navigate challenges effectively, companies must focus on improving data quality, investing in advanced algorithms, and addressing regulatory requirements. By doing so, they can differentiate themselves in a competitive landscape and drive innovation in the market.

What will be the Size of the Emotion Recognition and Sentiment Analysis Software Market during the forecast period?

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The market is experiencing significant growth, driven by the increasing adoption of conversational AI and virtual assistants. This technology enables the analysis of both textual and multimedia data, including audio and video, to extract emotional insights from user interactions. Data mining techniques, such as predictive modeling and model deployment, play a crucial role in processing and interpreting this data. Sentiment analysis dashboards and emotion recognition dashboards provide valuable insights into user experience, allowing businesses to map and optimize both the employee and customer journey. Cognitive computing and cognitive AI technologies are also integral to this market, enabling real-time analysis of user behavior and feedback.
Data ethics and responsible AI are becoming increasingly important considerations in this market, with a focus on data governance and model training to ensure accurate and explainable AI. Biometric data and behavioral data are also being leveraged to enhance the capabilities of emotion recognition systems, further expanding their applications. Model evaluation and model training are essential components of this market, ensuring the accuracy and effectiveness of AI models. Interpretable AI and explainable AI are also gaining traction, enabling businesses to understand the reasoning behind AI decisions and build trust in the technology. Data annotation and data annotation tools are critical for training AI models, ensuring high-quality data and accurate sentiment analysis.
Overall, the market is poised for continued growth, offering businesses valuable insights into user emotions and improving the user experience.

How is this Emotion Recognition and Sentiment Analysis Software Industry segmented?

The emotion recognition and sentiment analysis software industry 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 service/experience
  Product/market research
  Patient diagnosis
  Others


Deployment

  On-premises
  Cloud-based


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Rest of World (ROW)

By Application Insights

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

Emotion AI technology, integrated with sentiment analysis tools, is revolutionizing business operations by enabling real-time understanding of customer emotions and feedback. These solutions utilize machine learning, natural language processing, and computer vision to analyze text, voice, and facial expressions for sentiment scoring, emotion classification, and polarity analysis. Emotion lexicons and sentiment lexicons are used to identify and categorize emotions, while deep learning and predictive analytics provide insights into historical trends. Sentiment analysis plays a crucial role in various industries, including human resources for employee engagement and feedback analysis, fraud detection, and brand reputation management. It is also used in customer service to enhance customer experience through personalized communication and proactive issue resolution.

Social media monitoring and text analysis help businesses stay updated on brand mentions and customer sentiments, while voice analysis and tone analysis provide valuable insights from customer interactions. Integration with APIs, cloud computing, and data visualization tools streamlines the process, allowing for seamless implem