Parker, E.; Nasir, W.; Benson, O. Enhancing Emotion Detection with Sentiment Analysis Insights. Preprints2024, 2024040059. https://doi.org/10.20944/preprints202404.0059.v1
APA Style
Parker, E., Nasir, W., & Benson, O. (2024). Enhancing Emotion Detection with Sentiment Analysis Insights. Preprints. https://doi.org/10.20944/preprints202404.0059.v1
Chicago/Turabian Style
Parker, E., Wyne Nasir and Olivia Benson. 2024 "Enhancing Emotion Detection with Sentiment Analysis Insights" Preprints. https://doi.org/10.20944/preprints202404.0059.v1
Abstract
The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.
Keywords
sentiment analysis; transfer learning; natural language processing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
