An, Jieyu
(2024)
Multimodal Sentiment Analysis Of Social Media Through Deep Learning Approach.
PhD thesis, Perpustakaan Hamzah Sendut.
Abstract
Multimodal Data, Characterized By Its Inherent Complexity And Heterogeneity,
Presents Computational Challenges In Comprehending Social Media Content.
Conventional Approaches To Sentiment Analysis Often Rely On Unimodal Pre-Trained
Models For Feature Extraction From Each Modality, Neglecting The Intrinsic Connections
Of Semantic Information Between Modalities, As They Are Typically Trained On Unimodal
Data. Additionally, Existing Multimodal Sentiment Analysis Methods Primarily Focus On
Acquiring Image Representations While Disregarding The Rich Semantic Information
Contained Within The Images. Furthermore, Current Methods Often Overlook The
Significance Of Color Information, Which Provides Valuable Insights And Significantly
Influences Sentiment Classification. Addressing These Gaps, This Thesis Explores Deep
Learning-Based Methods For Multimodal Sentiment Analysis, Emphasizing The Semantic
Association Between Multimodal Data, Information Interaction, And Color Sentiment
Modelling From The Perspectives Of The Multimodal Representation Layer, The
Multimodal Interaction Layer, And The Color Information Integration Layer. To Mitigate
The Overlooked Semantic Interrelations Between Modalities, The Thesis Introduces "Joint
Representation Learning For Multimodal Sentiment Analysis" Within The
Representation Layer. This Method, Validated By Rigorous Experiments, Showcases A
Marked Improvement In Accuracy, Achieving 76.44% On The Mvsa-Single And 72.29%
On The Mvsa-Multiple Datasets, Surpassing Existing Methodologies. In The Multimodal
Interaction Layer,
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