Multimodal Sentiment Analysis on Product Review Text and Image Using Machine Learning
Mayank Devani, Harsha Padheriya, Vijaysinh Jadeja, Dr. Jaimin Jani, Arachana Patel · African Journal of Biomedical Research · 2024
Sentiment assessment is crucial for grasping consumer viewpoints on products, services, and brands [3]. Given the surge in online reviews across diverse platforms, there's an imperative for sophisticated technologies capable of precisely gauging sentiment. This study introduces a specialized multimodal sentiment evaluation [5] framework tailored for product assessments. By leveraging textual [1] and visual indicators, our method fuses natural language processing (NLP) methods with computer vision techniques to capture subtle emotional nuances. We employ pretrained linguistic and visual models for feature extraction and employ a fusion approach to amalgamate data from diverse sources. Through testing on comprehensive benchmark datasets, our method showcases its aptitude in predicting emotional sentiment with accuracy. Furthermore, we undertake comparative assessments against cutting-edge techniques to highlight the advantages of our proposed framework [3]. Our findings underscore the significance of multimodal strategies in enhancing the precision and resilience of product review sentiment analysis, furnishing businesses and stakeholders with insightful insights into consumer sentiments. Multimodal [5] sentiment analysis encompasses the evaluation of emotions conveyed through varied mediums, including text [1], images, audio, and video. As the volume of user-generated content on e-commerce sites and social networks continues to rise, there is an escalating interest in diversifying sentiment analysis methodologies.