Unsupervised Emotion Detection from Text using Semantic and Syntactic Relations

Emotion detection from text is a relatively new classification task. This paper proposes a novel unsupervised context-based approach to detecting emotion from text at the sentence level. The proposed methodology does not depend on any existing manually crafted affect lexicons such as WordNet-Affect, thereby rendering our model flexible enough to classify sentences beyond Ekman's model of six basic emotions. Our method computes an emotion vector for each potential affect-bearing word based on the semantic relatedness between words and various emotion concepts. The scores are then fine tuned using the syntactic dependencies within the sentence structure. Extensive evaluation on various data sets shows that our framework is a more generic and practical solution to the emotion classification problem and yields significantly more accurate results than recent unsupervised approaches.