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| Format: | Preprint |
| Published: |
2024
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| Online Access: | https://arxiv.org/abs/2411.10328 |
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| _version_ | 1866909391690661888 |
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| author | Alaeddini, Maliheh |
| author_facet | Alaeddini, Maliheh |
| contents | Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates Reddit comments with 27 distinct emotions. These emotions are subsequently mapped to Ekman's six basic categories: joy, anger, fear, sadness, disgust, and surprise. We employed a range of models for this task, including six machine learning models, three ensemble models, and a Long Short-Term Memory (LSTM) model to determine the optimal model for emotion detection. Results indicate that the Stacking classifier outperforms other models in accuracy and performance. We also benchmark our models against EmoBERTa, a pre-trained emotion detection model, with our Stacking classifier proving more effective. Finally, the Stacking classifier is deployed via a Streamlit web application, underscoring its potential for real-world applications in text-based emotion analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_10328 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques Alaeddini, Maliheh Computation and Language Emotion detection is pivotal in human communication, as it significantly influences behavior, relationships, and decision-making processes. This study concentrates on text-based emotion detection by leveraging the GoEmotions dataset, which annotates Reddit comments with 27 distinct emotions. These emotions are subsequently mapped to Ekman's six basic categories: joy, anger, fear, sadness, disgust, and surprise. We employed a range of models for this task, including six machine learning models, three ensemble models, and a Long Short-Term Memory (LSTM) model to determine the optimal model for emotion detection. Results indicate that the Stacking classifier outperforms other models in accuracy and performance. We also benchmark our models against EmoBERTa, a pre-trained emotion detection model, with our Stacking classifier proving more effective. Finally, the Stacking classifier is deployed via a Streamlit web application, underscoring its potential for real-world applications in text-based emotion analysis. |
| title | Emotion Detection in Reddit: Comparative Study of Machine Learning and Deep Learning Techniques |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2411.10328 |