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Texts, Images, and Emotions in Political Methodology
- Publication Year :
- 2022
-
Abstract
- My dissertation comprises (1) the development of a machine learning framework that combines verbal and visual features together, models the intricate web of relationships between them, and extracts visual semantics, and (2) the application of a deep learning and a transfer learning framework to extract emotions from social media posts. This dissertation consists of three papers as follows. The first paper introduces a machine-learning visual framing analysis to examine the visual and verbal patterns of online news reporting and explore image-text relations in news stories. The second paper presents a machine-learning multimodal framing analysis to integrate the various types of data (e.g., image, text, and metadata) simultaneously and extract the semantic meaning from them together. The third paper is an application of a deep learning and a transfer learning to show the power of Twitter in providing fine-grained measures of real-time emotions and thereby offer a comprehensive overview of the role of emotions in voting participation. My dissertation can take into account various types of data simultaneously and extract politically meaningful semantics using computer vision, NLP, graph theory, high-dimensional statistics, and transfer learning.
Details
- Language :
- English
- Database :
- OpenDissertations
- Publication Type :
- Dissertation/ Thesis
- Accession number :
- ddu.oai.etd.ohiolink.edu.osu1658489994363848