Identifying Emotional Expressions, Intensities and Sentence Level Emotion Tags Using a Supervised Framework

The present work deals with the extraction of emotional expressions and tagging of English blog sentences with Ekman’s six basic emotion tags and any of the three intensities: low, medium and high. Baseline system is developed based on WordNet Affect lists and dependency relations. Support Vector Machine (SVM) based supervised framework is employed by incorporating different word and context level features. The feature analysis is carried out on 358 development sentences followed by Information Gain Based Pruning. Application of admissible tag sequences and a class-splitting technique improves the system’s performance and reduces the label bias problem of SVM. The supervised system outperforms the baseline system and achieves average F-Scores of 82.72%, 76.74% and 89.21% for emotional expressions, sentential emotion tags and intensities respectively on 565 gold standard test sentences. A comparative evaluation shows that sentential emotion tagging based on emotional expressions, intensities and context features bridges the gap of identifying sentential emotion depending only on words.