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Abstract
Understanding emotions is one of the most important aspects of personal development and growth and, as such, it is a key tile for the emulation of human intelligence. Besides being a important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity automatically to capture the sentiments of the general public about social events, political movements, marketing campaigns, and product preferences, in fact, has raised increasing interest both in the scientific community, for the exciting open challenges, and in the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage on human-computer interaction, information retrieval, and multimodal signal processing for distilling people’s sentiments from the ever-growing amount of online social data.
Keywords
Affective computing • Sentiment analysis • Five eras of the Web • Jumping NLP curves • Hybrid approaches
Erik Cambria, Dipankar Das, Sivaji Bandyopadhyay, and Antonio Feraco
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