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Anti-vaccine sentiment classification of tweets

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thesis
posted on 28.03.2022, 02:30 authored by Azin Ramezani
An important public health issue is the spread of diseases that could be prevented by changing individual beliefs and opinions about vaccination. Monitoring the spread of people’s opinions through online social communities may be helpful for those public health purposes. This thesis builds on work on identifying negative sentiment about human papillomavirus (HPV) vaccines on Twitter using word n-grams and direct social connection information as features in a Support Vector Machine (SVM) classifier. This thesis examines four extensions to this. First, biological models have suggested that negative opinion is transmitted contagiously; we incorporate this by adding indirect social connection information via label propagation. Second, topic models are used to infer topics associated with tweets to reduce the feature space dimensionality in classification. Third, the content of web pages that are referenced in tweets are used as new features for classification. Finally, label propagation is extended by adding more features beyond social connection information, such as n-grams, topics, and linked web pages contents. All these extensions improve classification results to some extent, with label propagation particularly effective for tweets sent in the same time period, and topic models across longer time periods.

History

Table of Contents

1. Introduction -- 2. Literature review -- 3. Re-implementation -- 4. Label propagation -- 5. opic modelling -- 6. Incorporating URL contents with sentiment classification -- 7. Extended label propagation -- 8. Conclusion -- References.

Notes

Theoretical thesis. Bibliography: pages 54-58

Awarding Institution

Macquarie University

Degree Type

Thesis MRes

Degree

MRes, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award

2016

Principal Supervisor

Mark Dras

Rights

Copyright Azin Ramezani 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (x, 58 pages) diagrams, graphs, tables

Former Identifiers

mq:70252 http://hdl.handle.net/1959.14/1261763