Yolo Algorithm Based Multimodal Sentiment Analysis on Image and Text Data

Yolo Algorithm Based Multimodal Sentiment Analysis on Image and Text Data

Publication Date : 2023-07-13
Author(s) :

J. Sindhuja A, C. Brintha
Conference Name :

7th International Conference on Recent Innovations in Computer and Communication (ICRICC 23)
Abstract :

Sentiment analysis’s objective is to examine public sentiment in a way that will support corporate growth. It emphasizes emotions as well as polarity (positive, negative, and neutral). It makes use of a variety of Natural Language Processing techniques, including Automatic, Hybrid, and Rule based. Users are becoming accustomed to uploading text and photographs on social networks to express their feelings or ideas. As a result, multimodal sentiment analysis has drawn more attention as a research area in recent years. Usually, an image has emotional areas that trigger human emotion, which are typically expressed by corresponding words in comments. Similarly, while writing visual descriptions, people frequently depict the emotive areas of an image. As a result, for multimodal sentiment analysis, the association between picture affective areas and the accompanying text is extremely important. This paper exhibits one of the best CNN representatives You Only Look Once (YOLO), which breaks through the CNN family’s tradition and innovates a completely new way of solving object detection with most simple and highly efficient way. Its name derives from the fact that, unlike earlier object detector algorithms like Faster RCNN, it only requires that an image or video travel once through its network. Its outcomes surpassed the performance of Faster RCNN greatly. The performance of YOLO is compared with faster RCNN in terms of accuracy and F1 Measure.

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