Utilizing Deep Learning Techniques for Sentiment Analysis during Disasters

Uppalapati, Mahesh Kumar (2023) Utilizing Deep Learning Techniques for Sentiment Analysis during Disasters. Masters thesis, Dublin, National College of Ireland.

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Abstract

The rise of social media has had a profound impact on sentiment analysis. With the widespread use of platforms like Twitter, Facebook, and Instagram, individuals can quickly and effortlessly express their thoughts, emotions, and responses to various events, including natural disasters. As a result, researchers have recognized the immense potential of social media data in understanding current public sentiment. Yet, the rapid growth of data and the need for comprehensive analysis have led to the use of more advanced methodologies. People today have the option to research topics outside of their social group. The availability of user reviews and public forums on the Internet has similarly freed businesses and organizations from relying on surveys and polls to acquire product ratings. Using machine learning to identify the tone of online comments has numerous practical uses and commercial appeals. It has been used in everything from consumer goods and services to healthcare and finance to social gatherings and political campaigns to, more lately, crisis management and natural disasters. In this work, to make predictions on the Twitter earthquake disaster dataset, we use machine learning models like Naive Bayes, Support Vector Machines (SVMs), and deep learning models like LSTMs. Our experimental results were able to demonstrate the superiority of deep learning models over machine learning models in all the evaluation metrics.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rifai, Hicham UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	23 May 2025 14:45
Last Modified:	23 May 2025 14:45
URI:	https://norma.ncirl.ie/id/eprint/7628

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