Harnessing Character-level Dynamics and Word-level Semantics: A New Perspective on Twitter Sentiment Analysis

In recent years, Sentiment Analysis has emerged as an indispensable component of social media analysis. It not only allows understanding public opinion on various issues, but also aids in monitoring real-time trends and sentiments, critical for domains such as brand management, market research, political campaign management, and crisis management. Central to the effectiveness of sentiment analysis is the extraction of meaningful features from the text data, with sentence embeddings being a vital component in capturing semantic relationships and sentence structures. This paper presents a novel approach to Twitter sentiment analysis that exploits sentence embeddings derived from Dynamic Mode Decomposition (DMD). DMD, a dimensionality reduction technique, enables us to capture the underlying dynamics of character transitions in tweets and to construct expressive sentence embeddings. The advantage of DMD lies in its ability to represent complex patterns and dependencies within the tweet text, thereby improving the richness of the derived sentence embeddings. Additionally, we investigate the use of Graph Neural Networks (GNNs) for sentiment classification, harnessing the power of relational modeling in the context of Twitter data. We also delve into word-level analysis with different emotion-based embeddings, assessing their impact on the sentiment classification performance. Our methodology integrates character-level analysis, graph-based modeling, and word-level semantics, providing a comprehensive, multi-layered approach to sentiment analysis on Twitter. Our experiments yield insightful results, highlighting the potential of DMD-based embeddings and the importance of high-quality, context-rich word embeddings in sentiment analysis tasks. These findings contribute to our understanding of sentiment analysis on social media platforms and suggest promising directions for future research in this field.