Israa K. Salman Al-Tameemi1,3, Mohammad-Reza Feizi-Derakhshi1,*, Saeed Pashazadeh2, Mohammad Asadpour2
CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2145-2177, 2023, DOI:10.32604/cmc.2023.040997
Abstract Multimodal Sentiment Analysis (SA) is gaining popularity due to its broad application potential. The existing studies have focused on the SA of single modalities, such as texts or photos, posing challenges in effectively handling social media data with multiple modalities. Moreover, most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations, leading to unsatisfactory sentiment classification results. Motivated by this, we propose a new visual-textual sentiment classification model named Multi-Model Fusion (MMF), which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual… More >
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