A Systematic Literature Review of Emotion Recognition in Conversation Using Natural Language Processing
Volume: 14 - Issue: 12 - Date: 01-12-2025
Approved ISSN: 2278-1412
Published Id: IJAECESTU474 | Page No.: 110-118
Author: Indresh Yadav
Co- Author: Kamlesh Raghuwanshi, Dr. Surabhi Karsoliya,,
Abstract:-Text-based emotion detection, especially in multi-speaker conversations, is now a very important area of research in
Natural Language Processing (NLP). It has many useful uses in fields like customer service, healthcare, and social media
analysis. This systematic literature review compiles the development of methodologies in this domain, mapping the transition
from fundamental techniques to the contemporary state of the art. We start by looking at early lexicon-based and rule-based
methods. These were fast to compute, but they had trouble with the subtleties of conversational language. The review then talks
about how machine learning models like Support Vector Machines and Naive Bayes classifiers have made things better. These
models used data-driven pattern recognition, but they were often limited by features that were made by hand. Deep learning is at
the heart of modern emotion detection. Sequential models like LSTMs and transformer-based architectures like BERT are setting
new performance standards by capturing temporal dependencies and bidirectional context. We also talk about hybrid and
ensemble models that use more than one method to make predictions more accurate. This review also shows how important
multimodal and contextual approaches are becoming. These approaches combine text with audio-visual cues and model how
conversations work. Even though a lot of progress has been made, problems like dataset dependency, multilingual support, and
ethical biases still exist. This paper concludes by delineating these challenges and charting future research trajectories,
underscoring the necessity for more resilient, multimodal, and ethically-informed systems to enhance human-computer
interaction.
Key Words:-Emotion Detection, Natural Language Processing (NLP), Emotion Recognition in Conversation (ERC), Deep Learning, Machine Learning, Sentiment Analysis, Multimodal Emotion Recognition.
Area:-Engineering
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