Enhancing Sentimental analysis using Multimodal data
Main Article Content
Abstract
Sentiment analysis is crucial for assessing opinions regarding a variety of topics. It includes text, audio, and video. It assesses brand sentiment and customer happiness in detail. The emergence of multimodal Sentiment Analysis, which incorporates data streams beyond text, can be attributed to the advent of social media. Multimodal models aspire to cultivate a more profound and comprehensive understanding of the data, paving the way for novel insights and unlocking diverse applications. It is a powerful tool for sentiment identification that includes audio, photos, voice expressions, and more. Applications include everything from human-machine interactions to video blogs. The proposed sentiment analysis model aims to detect sentiments in video highlights using time-sync comments, departing from the conventional method that relies solely on basic sentiment word combinations and coarse sentiment categorization. This approach seeks to overcome the limitations of traditional machine learning classification techniques. In this article, we propose modern technologies like HMM, CNN, and MFCC for more accurate results. the information we gather from all three categories audio, video, and text so the cluster of all data helps us to find the better results. In this article Sentiment analysis across several domains is a constantly developing field that presents opportunities and challenges for further research.
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