Natural Affect DEtection (NADE): Using Emojis to Infer Emotions from Text

Emotions are central to consumer communications, and extracting them from user-generated online content is crucial for marketers, given that such consumer opinions significantly shape brand perceptions, influence purchase decisions, and provide essential insights for marketing analytics. To leverage vast user-generated data, marketers and researchers require advanced text-to-emotion converters. However, existing tools for fine-grained emotion extraction face several limitations: Lexica are constrained by their dictionaries, machine learning models by human-annotated training data, and large language models by insufficient validation. As a result, marketing research still tends to rely on mere sentiment detection instead of extracting more nuanced emotions from text. This article introduces NADE (Natural Affect DEtection), a novel text-to-emoji-to-emotion converter that first “emojifies” language and then converts these emojis into intensity measures of well-established, theory-grounded emotions. This approach addresses the limitations of existing tools by leveraging the inherent emotional information in emojis. Using human raters and state-of-the-art converters as benchmarks, the authors establish the benefits of exploiting emojis, validate NADE, and demonstrate its use in several marketing applications using data from various social media platforms. Users can apply the proposed converter through an easy-to-use online app and programming packages for Python and R.