Humans use a variety of modifiers while expressing their emotions. This project aims to understand the influence of different modifiers on specific emotion categories, and to compare side-by-side their impact in an automatic manner. To do so, we propose a novel data analysis method that not only quantifies how much emotional statements change under each modifier, but also models how emotions shift and how their confidence changes. This method is based on comparing the distributions of emotion labels for modified and non-modified occurrences of emotional terms within labeled data. We apply this analysis to study six types of modifiers (negation, intensification, conditionality, tense, interrogation, and modality) within a large corpus of tweets with emotional hashtags. Our study sheds light on how to model negation relations between studied emotions, reveals the impact of previously under-studied modifiers, and suggests how to detect more precise emotional statements.
Emotion Recognition, Linguistic Modifiers, Text Analysis, Twitter
- Valentina Sintsova, Margarita Bolivar Jimenez, Pearl Pu. Modeling the Impact of Modifiers on Emotional Statements. CICLing, 2017 (to appear)