EECE 571T: Advanced Machine Learning Tools
Course Project
Project Title: Comparison of Different Machine Learning Algorithms with Sentiment Analysis
The main course work in this course is the course project, which is a self-defined project. In this course, my partner and I chose to compare different modern machine learning algorithms in the field of natural language processing.
Project source code available via GitHub:
- Project GitHub repository: https://github.com/tkjsung/EECE571T-Project
- Project dataset GitHub repository: https://github.com/tkjsung/EECE571T-Dataset
Project Abstract
A comparison of different machine learning algorithms for sentence-level sentiment analysis problem on a Kaggle dataset was performed. Sentiment analysis (SA) is a subfield of Natural Language Processing (NLP) and has been widely used in everyone’s daily lives, ranging from brand watch to social media monitoring. Thus, a model with high accuracy of SA has many important practical applications. In this report, several SA models are tested, including traditional non-neural network-based method and neural-network based methods. In particular, the traditional method that was examined was support vector machine (SVM) and the neural network-based methods include convolutional neural network (CNN), recurrent neural network (RNN), and bidirectional encoder representations from transformers (BERT). The test results from these networks show that performance is limited with neural network models. It was also found that while CNN is good for computer vision applications, it is not good for sentiment analysis. Lastly, for complex models like BERT, a large amount of training time and resources are required to achieve the best performance.
Course Info
Text in this section is taken from the website of the Department of Electrical and Computer Engineering at the University of British Columbia (https://ece.ubc.ca). Text was extracted in 2022.
This is an applied course on Machine Learning (ML) and Deep Learning covering:
- Clustering and dimensionality reduction algorithms.
- Classification and regression algorithms.
- Introduction to deep learning.
- Deep learning in computer vision and time series.
- Advanced machine learning algorithms.
Learning Objectives: By the end of this course, students will be able to:
- Recognize machine learning problems in their domain of interest.
- Formulate a corresponding ML problem and solve it with appropriate tools.
- Use Python and Jupyter notebooks to solve machine learning problems;