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oai:keep.lib.asu.edu:node-2023272025-08-18T22:22:09Zoai_pmh:alloai_pmh:repo_items

202327 https://hdl.handle.net/2286/R.2.N.202327 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2025 59 pages Masters Thesis Academic theses en Srinivasan, Shreyas Davulcu, Hasan Liu, Huan Yang, Yezhou Cetinkaya, Yusuf Mucahit Arizona State University Partial requirement for: M.S., Arizona State University, 2025 Field of study: Computer Science Aspect Based Sentiment Analysis (ABSA) offers fine-grained sentiment detection by identifying opinions tied to specific aspects within text. However, most existing ABSA models are unimodal, relying solely on textual data and struggling with subtle expressions like sarcasm or weak opinions, which is common in real-world discourse. This limitation highlights the value of incorporating non-verbal cues, such as facial emotions, to better capture the emotional context. This work presents a novel Multimodal ABSA model that integrates textual embeddings from MaskedABSA with facial emotion features extracted using EMO-AffectNet, which are then passed through a temporal model. This enhances sentiment understanding by aligning semantic and affective cues. A custom-curated dataset centered on the Black Lives Matter (BLM) and All Lives Matter (ALM) discourse is introduced, annotated using a structured codebook to provide stance-specific supervision. Compared to unimodal and a state of the art vision language baseline, the proposed model demonstrates consistent improvements in both accuracy and F1 Score, achieving a relative increase of approximately 13% in accuracy and over 10% in F1 Score compared to a text only ABSA model. These results underscore the effectiveness of multimodal integration for accurate stance detection and its potential for promoting ethical, context aware recommendation systems. Computer Science Artificial Intelligence ABSA Emotion Machine learning multimodal Social Multimodal Aspect Based Sentiment Analysis with Emotion Fusion in Social Discourse