Abstract
Generic sentiment and emotion lexica are widely used for the fine–grained analysis of human affect from text. In order to accurately detect affect, there is a need for domain intelligence, that enables understanding of the perceived interpretation of the same words in varied contexts. Recent work has focused on automatically inducing the polarity of given terms in changing contexts. We propose an unsupervised approach for the construction of domain–specific affect lexica along these lines. The algorithm is seeded with existing standard lexica and expanded based on context–relevant word associations. Experiments show that our lexicon provides better coverage than standard lexica on both short as well as long texts, and corresponds well with human–annotated affect values. Our framework outperforms the state–of–the–art generic and domain–specific approaches with a precision of over 70% for the emotion detection task on the SemEval 2007 Affect Corpus.
K. Jaidka—This work was done when all authors were at Adobe Research. All authors have equal contribution in this paper.
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Jaidka, K., Chhaya, N., Wadbude, R., Kedia, S., Nallagatla, M. (2018). BATframe: An Unsupervised Approach for Domain-Sensitive Affect Detection. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_2
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