A comparison of Long Short-Term Memory, Convolutional Neural Network, Transformer, and Mamba models for sentiment analysis
| dc.contributor.author | Ruan, Hang | |
| dc.contributor.supervisor | Gulliver, Thomas Aaron | |
| dc.date.accessioned | 2024-10-21T19:18:51Z | |
| dc.date.available | 2024-10-21T19:18:51Z | |
| dc.date.issued | 2024 | |
| dc.degree.department | Department of Electrical and Computer Engineering | |
| dc.degree.level | Master of Engineering MEng | |
| dc.description.abstract | Sentiment analysis is a critical task in Natural Language Processing (NLP) that helps decode the emotions and opinions embedded in text. With applications spanning from market research and social media monitoring to political analysis and customer feedback evaluation, sentiment analysis provides invaluable insights into public opinion and consumer behavior. This project studies the evolution of sentiment analysis models, focusing on the advancements made by deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT). These models have set new benchmarks for accuracy, efficiency, and versatility. Additionally, this explores Mamba, a recent State Space Model (SSM) designed to overcome the computational challenges of transformers in handling long sequences and demonstrates state-of-the-art performance on language modeling tasks comparable to transformers twice its size. This study examines the strengths and limitations of these models, comparing their performance on sentiment analysis datasets to provide a comprehensive understanding of their applicability and efficacy in various contexts. | |
| dc.description.scholarlevel | Graduate | |
| dc.identifier.uri | https://hdl.handle.net/1828/20610 | |
| dc.language.iso | en | |
| dc.subject | LSTM | |
| dc.subject | CNN | |
| dc.subject | Transformer | |
| dc.subject | Mamba | |
| dc.subject | sentiment analysis | |
| dc.title | A comparison of Long Short-Term Memory, Convolutional Neural Network, Transformer, and Mamba models for sentiment analysis | |
| dc.type | project |