Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus

Abstract

Predicting emotion categories (e.g. anger, joy, sadness) expressed by a sentence is challenging due to inherent multi-label smaller pieces such as phrases and clauses. To date, emotion has been studied in single genre, while models of human behaviors or situational awareness in the event of disasters require emotion modeling in multi-genres. In this paper, we expand and unify existing annotated data in different genres (emotional blog post, news title, and movie reviews) using an inventory of 8 emotions from Plutchik's Wheel of Emotions tags. We develop systems for automatically detecting and classifying emotions in text, in different textual genres and granularity levels, namely, sentence and clause levels in a supervised setting. We explore the effectiveness of clause annotation in sentence-level emotion detection and classification (EDC). To our knowledge, our EDC system is the first to target the clause level; further we provide emotion annotation for movie reviews dataset for the first time.

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