Enhancing sentiment analysis with distributional emotion embeddings

Sentiment classification tasks, such as emotion detection and sentiment analysis, are essential in modern natural language processing (NLP). Moreover, vector representation frameworks modeling semantic content underlie each state-of-the-art NLP algorithmic scheme. In sentiment classification, traditional methods often rely on such embedding vectors for semantic representation, yet they typically overlook the dynamic and sequential nature of emotions within textual data. In this work, we present a novel methodology that leverages the distributional patterns of emotions. An embedding framework that captures the inherent serial structure of emotional occurrences in text is introduced, modeling the interdependencies between emotion states as they unfold within a document. Our approach treats each sentence as an observation in a multivariate series of emotions, transforming the emotional flow of a text into a sequence of emotion strings. By applying distributional logic, emotion-based embeddings that represent both emotional and semantic information are derived. Through a comprehensive experimental framework, we demonstrate the effectiveness of these embeddings across various sentiment-related tasks, including emotion detection, irony identification, and hate speech classification, evaluated on multiple datasets. The results show that our distributional emotion embeddings significantly enhance the performance of sentiment classification models, offering improved generalization across diverse domains such as financial news and climate change discourse. Hence, this work highlights the potential of using distributional emotion embeddings to advance sentiment analysis, offering a more nuanced understanding of emotional language and its structured, context-dependent manifestations.