International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 07 | Jul 2023
p-ISSN: 2395-0072
www.irjet.net
Contextual Emotion Recognition Using Transformer-Based Models Aayush Devgan1 1Student, Computer Science Department, VIT University, Vellore, Tamil Nadu
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Abstract - In order to increase the precision of emotion
model, which has a simple yet effective design and is computationally realistic for real-world applications.
identification in text, this research suggests a context-aware emotion recognition system employing transformer models, especially BERT. The model is able to comprehend complex emotions and context-dependent expressions since it was trained on a broad, emotion-labeled dataset. On a benchmark dataset, its efficacy is assessed compared to conventional techniques and standard transformer models. The system is proficient at gathering contextual information, and the findings demonstrate a considerable improvement in emotion recognition accuracy. This study improves textual emotion identification, opening the door to applications like chatbots that can recognize emotions and systems for tracking mental health. It also identifies potential areas for further study in developing transformer models for context-sensitive NLP applications.
The suggested system seeks to overcome the drawbacks of existing emotion identification techniques, which frequently fail to take context into account and mainly rely on lexicon-based methods. The fine-grained connections between words within their context may be captured by transformer models, which, in contrast, can develop complicated representations from unprocessed text input. The system becomes better at understanding the subtle differences in emotions communicated via various language expressions by including context awareness in the emotion recognition process. I make use of a broad and annotated emotion-labeled dataset to assess the effectiveness of our context-aware emotion identification algorithm. This dataset covers many domains to ensure the model's flexibility across diverse settings and writing styles and includes literary texts, product evaluations, and social media postings. I contrast the effectiveness of our context-aware method with that of conventional transformer-based models that do not specifically address emotion detection and classic emotion identification methods.
Key Words: Deep learning; Machine learning; BERT; Transformer; Sentiment Analysis
1. INTRODUCTION As a basic component of human communication, emotion has a significant influence on how we communicate, make decisions, and perceive the world. From marketing and consumer feedback analysis to mental health support systems and virtual assistants, understanding and properly identifying emotions from the text have become crucial in various industries. The complexity of language and the subtle differences in how emotions are expressed in various settings make it difficult to recognize emotions from the text.
I anticipate enabling a wide range of applications by creating an emotion identification system that can efficiently exploit contextual information. Chatbots with emotional intelligence may react sympathetically to users' emotional states, improving user interaction and engagement. Systems for recommending personalized material should better comprehend users' emotional preferences and adapt content accordingly. The possible applications also include tools for tracking users' mental health, where an emotion-aware system might help spot patterns of emotional discomfort in their textual expressions.
Transformer models' state-of-the-art performance in a variety of language-related tasks has recently revolutionized natural language processing. These models, like BERT (Bidirectional Encoder Representations from Transformers) and its offshoots, have shown to be very good at capturing complex semantic linkages and contextual dependencies in text data. The accuracy and robustness of emotion detection systems may be greatly improved by utilizing transformer-based models for emotion recognition.
Finally, this study contributes to the continuing work to close the cognitive gap between language comprehension and emotion perception. It is possible to alter emotion detection in text and its applications in real-world contexts by including transformer models with context awareness. I expect that this context-aware strategy will open the door for more intuitive and sympathetic interactions between people and AI systems, leading to a deeper understanding of human emotions through textual data as I delve further into the field of emotion-aware natural language processing.
This study examines the creation of a transformer modelbased system for context-aware emotion identification. The main goal is to increase the sensitivity and accuracy of emotion recognition by taking advantage of the contextual information in the text. I concentrate on using the BERT
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