Event Details
A Comparison of LSTM, CNN, Transformer, and Mamba Models for Sentiment Analysis
Presenter: Hang Ruan
Supervisor:
Date: Thu, October 10, 2024
Time: 09:00:00 - 00:00:00
Place: Zoom, link below.
ABSTRACT
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 report 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 work 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 that is comparable to transformers twice its size. This study critically 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.
Topic: Zoom Meeting
Time: Oct 10, 2024 09:00 AM Pacific Time (US and Canada)
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Meeting ID: 811 8553 3518
Password: 090223
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