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Text-Based Emotion Recognition Using Deep Learning Approach.

Santosh Kumar Bharti1, S Varadhaganapathy2, Rajeev Kumar Gupta1

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Summary
This summary is machine-generated.

This study introduces a hybrid model combining machine learning and deep learning for text-based emotion detection. The novel approach achieves 80.11% accuracy in identifying emotions from diverse text forms.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Emotion detection from text is challenging due to the absence of non-verbal cues like tone or facial expressions.
  • Traditional methods like keyword and lexicon-based approaches have limitations in capturing nuanced semantic relations.
  • Existing research has focused on speech and facial expressions, leaving text-based emotion recognition as a complex task.

Purpose of the Study:

  • To propose a hybrid model integrating machine learning and deep learning for enhanced text-based emotion detection.
  • To overcome the limitations of conventional sentiment analysis and emotion recognition techniques in textual data.
  • To develop a robust model capable of identifying specific emotions beyond simple positive, negative, or neutral classifications.

Main Methods:

  • A hybrid model was developed, combining deep learning techniques (Convolutional Neural Network and Bi-directional Gated Recurrent Unit) with a machine learning approach (Support Vector Machine).
  • The model was trained and evaluated on a diverse dataset comprising sentences, tweets, and dialogs.
  • Natural Language Processing (NLP) techniques formed the foundation for text analysis.

Main Results:

  • The proposed hybrid model achieved a significant accuracy of 80.11% in emotion detection.
  • The model demonstrated effectiveness across various text types, including informal (tweets) and conversational (dialogs) data.
  • The integration of deep learning and machine learning approaches proved superior to traditional methods.

Conclusions:

  • The hybrid machine learning and deep learning model offers a promising solution for accurate emotion detection in text.
  • This approach effectively addresses the complexities of identifying emotions from textual data, outperforming previous methods.
  • The model's high accuracy across different text formats highlights its potential for real-world applications in sentiment analysis and beyond.