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Sentiment analysis of text plays a crucial role in various fields, particularly in marketing and customer service industries, where understanding subjective informa- tion from text data is essential. While existing sentiment analysis tools often focus on binary classifications of positive or negative sentiment, this study delves into the possibility of representing emotions using multiple dimensions. By exploring Ekman’s six basic emotions and the Valence- Arousal-Dominance (VAD) structure, this research aims to investigate whether using more than one dimension to classify emotions is useful. Two datasets, Bag-of-Words and EmoBank, are analyzed, with EmoBank providing VAD values for 10,000 English sentences. Research questions focus on optimizing textual sentiment prediction and evalu- ating the utility of multi-dimensional emotion classification. Experimental investigations involve data pre-processing, model selection, and sampling tests to address dataset limitations and dependencies between variables. Findings suggest the potential for building more nuanced senti- ment prediction models, with implications for improving sentiment analysis accuracy and understanding human emotions in text data.
Milind Cherukuri (Fri,) studied this question.