A Novel Machine Learning Approach for Analyzing Subtly Expressed Opinions in Diverse Online Platforms

Abstract

Various applications have grown to rely on comprehending the ideas and feelings conveyed in user-generated content because to the exponential expansion of this type of data on internet platforms. But it's not easy to identify and understand sarcasm and implicit emotions and other nuanced statements of opinion across different language modes. This study introduces a fresh method using machine learning to tackle this problem. We present a model that integrates deep learning, multi-modal analysis, and natural language processing to pick up on subtle sentiments conveyed in visual content like photos and films shared online. We show that our method is effective in sarcasm detection, emotion identification, and opinion analysis tasks by evaluating it on several datasets from different web platforms. In addition, we go over some of the possible uses and consequences of our research in domains including online content regulation, social media monitoring, and product review analysis. The suggested multi-modal and multi-task framework demonstrated good results in sarcasm detection, emotion recognition, and sentiment analysis whose F1-score ratings were of more than 0.80 in all the tasks. Attention based fusion showed the need to dynamically weight the importance of the text, audio, and visual modalities and ablation experiments were used to prove the fact that a combination of multiple modalities enhances the predictive accuracy greatly. This implies that the model is effective in the ability to elicit subtle and nuanced opinions in various online platforms.