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

Identifying Emotional Expressions, Intensities and Sentence level Emotion Tags using a Supervised Framework* Dipankar Das and Sivaji Bandyopadhyay Department of Computer Science and Engineering, Jadavpur University, 188, Raja S.C. Mullick Road, Kolkata -700032, West Bengal dipankar.dipnil2005@gmail.com, sivaji_cse_ju@yahoo.com Abstract. 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. Keywords: Emotional Expression, Intensity, Negation, Conjuncts, Information Gain. * The work reported in this paper was supported by a grant from the India-Japan Cooperative Programme (DST- JST) 2009 Research project entitled “Sentiment Analysis where AI meets Psychology” funded by Department of Science and Technology (DST), Government of India. Copyright 2010 by Dipankar Das and Sivaji Bandyopadhyay 1 Introduction In psychology and common use, emotion is an aspect of a person's mental state of being, normally based in or tied to the person’s internal (physical) and external (social) sensory feeling (Zhang et al., 2008). Emotion identification from natural language texts has drawn the attention of several information processing communities, e.g. reviews (Turney, 2002), news (Lin et al., 2007), Question Answering (Wilson et al., 2005), Information Retrieval (Pang and Lee, 2008). Several efforts have been made by the natural language processing researchers to identify emotion at different level of granularities such as word, sentence or document (Ku et al., 2006; Das and Bandyopadhyay, 2009b; Das and Bandyopadhyay, 2010). But in many domains of text, the values of individual phrases may bear little relation to the overall sentiment expressed by the text. Just like words, phrases are considered as the informative and emotion expressive units of any sentence and are used in identifying document level emotion tags (McDonald et al., 2007). Hence, the current dependence on individual word level information to identify sentential emotion has motivated the search for phrase level clues. The present task extracts the emotional expressions (word/phrase) from each of the English blog sentences and assigns any of the Ekman’s (1993) six basic emotion tags along with any of the three intensity types: low, medium and high to each sentence. Blog is one of the crucial, PACLIC 24 Proceedings 95 communicative and informative repository of text based emotional contents in the Web 2.0 (Lin et al., …

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