EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Lublin University of Technology
2024-12-01
|
| Series: | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| Subjects: | |
| Online Access: | https://ph.pollub.pl/index.php/iapgos/article/view/6679 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850103041094582272 |
|---|---|
| author | Raga Madhuri Chandra Giri Venkata Sai Tej Neelaiahgari Satya Sumanth Vanapalli |
| author_facet | Raga Madhuri Chandra Giri Venkata Sai Tej Neelaiahgari Satya Sumanth Vanapalli |
| author_sort | Raga Madhuri Chandra |
| collection | DOAJ |
| description |
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.
|
| format | Article |
| id | doaj-art-93763e317d2846ff9dcc7328c89c5d97 |
| institution | DOAJ |
| issn | 2083-0157 2391-6761 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Lublin University of Technology |
| record_format | Article |
| series | Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska |
| spelling | doaj-art-93763e317d2846ff9dcc7328c89c5d972025-08-20T02:39:38ZengLublin University of TechnologyInformatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska2083-01572391-67612024-12-0114410.35784/iapgos.6679EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGYRaga Madhuri Chandra0https://orcid.org/0000-0002-3687-0783Giri Venkata Sai Tej Neelaiahgari1https://orcid.org/0009-0009-5785-0413Satya Sumanth Vanapalli2https://orcid.org/0009-0006-5993-3456Velagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and EngineeringVelagapudi Ramakrishna Siddhartha Engineering College, Department of Computer Science and Engineering 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. https://ph.pollub.pl/index.php/iapgos/article/view/6679Emotional intelligenceEmotion-Cause Pair Extraction (ECPE)Bidirectional Long Short-Term MemoryEmotional dynamicsNatural language processingConversational Analysis |
| spellingShingle | Raga Madhuri Chandra Giri Venkata Sai Tej Neelaiahgari Satya Sumanth Vanapalli EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska Emotional intelligence Emotion-Cause Pair Extraction (ECPE) Bidirectional Long Short-Term Memory Emotional dynamics Natural language processing Conversational Analysis |
| title | EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY |
| title_full | EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY |
| title_fullStr | EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY |
| title_full_unstemmed | EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY |
| title_short | EXTRACTING EMOTION-CAUSE PAIRS: A BILSTM-DRIVEN METHODOLOGY |
| title_sort | extracting emotion cause pairs a bilstm driven methodology |
| topic | Emotional intelligence Emotion-Cause Pair Extraction (ECPE) Bidirectional Long Short-Term Memory Emotional dynamics Natural language processing Conversational Analysis |
| url | https://ph.pollub.pl/index.php/iapgos/article/view/6679 |
| work_keys_str_mv | AT ragamadhurichandra extractingemotioncausepairsabilstmdrivenmethodology AT girivenkatasaitejneelaiahgari extractingemotioncausepairsabilstmdrivenmethodology AT satyasumanthvanapalli extractingemotioncausepairsabilstmdrivenmethodology |