Abstract
Social networks have become prolific platforms for individuals to express their thoughts, emotions, and opinions, generating an unprecedented volume of user-generated content. However, traditional sentiment analysis methods mainly focus on textual data, disregarding valuable emotional cues conveyed through other modalities such as images, videos, and emojis. In response, this paper introduces "Sentiment Fusion," a novel approach harnessing the power of big data and deep learning for multimodal sentiment analysis in social networks. By aggregating diverse data sources and integrating deep learning techniques, the Sentiment Fusion model effectively extracts features from text, visuals, and emoticons to capture nuanced emotional nuances. Extensively evaluated on a large-scale dataset from popular social networks, the model outperforms single-modal approaches, providing more accurate sentiment analysis while offering interpretability insights. With scalability demonstrated, Sentiment Fusion paves the way for a deeper understanding of collective emotions on a global scale and finds applications in marketing, public opinion analysis, and social media monitoring. As part of future research, exploring additional performance metrics like precision, recall, F1-score, and cross-modal correlation will enable further refinement of the Sentiment Fusion model, enhancing its applicability and robustness in diverse real-world scenarios.
Published Version
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