EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY

Raga Madhuri Chandra

chragamadhuri@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0000-0002-3687-0783

Giri Venkata Sai Tej Neelaiahgari


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0009-5785-0413

Satya Sumanth Vanapalli


Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering (India)
https://orcid.org/0009-0006-5993-3456

Abstract

Emotions are fundamental to human interactions, intricately influencing communication, behavior, and perception. Emotion-Cause Pair Extraction (ECPE) is a critical task in natural language processing that identifies clause pairs associating emotions with their corresponding triggers within textual documents. Unlike traditional Emotion Cause Extraction (ECE), which relies on pre-annotated emotion clauses, our study introduces a novel end-to-end model for ECPE. This innovative approach utilizes the extensive NTCIR-13 English Corpus to establish a robust baseline for ECPE in English, showcasing significant performance improvements over conventional multi-stage methods. Central to our model is the incorporation of Bidirectional Long Short-Term Memory (BiLSTM) networks, enhancing the ability to capture both local and global dependencies in textual sequences. By effectively combining contextual and positional embeddings, our model accurately predicts emotion-cause relationships, paving the way for a deeper understanding of emotional dynamics in conversational contexts and facilitating causal inference. Furthermore, our research highlights superior performance metrics, aligning its efficacy with state-of-the-art techniques in the field. This study advances emotion recognition in natural language processing, providing valuable insights for nuanced analyses of human emotions within textual data. Additionally, our findings enhance understanding of emotional intelligence in user interaction modeling and conversational AI applications. Through the public availability of our dataset and model, we aim to foster collaboration and further research in this vital area, ultimately improving the capacity for emotional understanding in applications ranging from sentiment analysis to interactive learning.


Keywords:

Emotional intelligence, Emotion-Cause Pair Extraction (ECPE), Bidirectional Long Short-Term Memory, Emotional dynamics, Natural language processing, Conversational Analysis

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Published
2024-12-21

Cited by

Chandra, R. M., Neelaiahgari, G. V. S. T., & Vanapalli, S. S. (2024). EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 14(4), 97–103. https://doi.org/10.35784/iapgos.6679

Authors

Raga Madhuri Chandra 
chragamadhuri@vrsiddhartha.ac.in
Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0000-0002-3687-0783

Authors

Giri Venkata Sai Tej Neelaiahgari 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0009-5785-0413

Authors

Satya Sumanth Vanapalli 

Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering India
https://orcid.org/0009-0006-5993-3456

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