Abstract
Detecting depression or personality traits, tutoring and student behaviour systems, or identifying cases of cyber-bulling are a few of the wide range of the applications, in which the automatic detection of emotion is crucial. This task can contribute to the benefit of business, society, politics or education. The main objective of our research is focused on the improvement of the supervised emotion detection systems developed so far, through the definition and implementation of a technique to annotate large scale English emotional corpora automatically and with high standards of reliability. Our proposal is based on a bootstrapping process made up two main steps: the creation of the seed using NRC Emotion Lexicon and its extension employing the distributional semantic similarity through words embeddings. The results obtained are promising and allow us to confirm the soundness of the bootstrapping technique combined with the word embedding to label emotional corpora automatically.
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Acknowledgment
This research has been supported by the FPI grant (BES-2013-065950) and the research stay grant (EEBB-I-15-10108) from the Spanish Ministry of Science and Innovation. It has also funded by the Spanish Government (DIGITY ref. TIN2015-65136-C02-2-R) and the Valencian Government (grant no. PROMETEOII/2014/001).
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Canales, L., Strapparava, C., Boldrini, E., Matínez-Barco, P. (2017). Bootstrapping Technique + Embeddings = Emotional Corpus Annotated Automatically. In: Quesada, J., Martín Mateos , FJ., López Soto, T. (eds) Future and Emerging Trends in Language Technology. Machine Learning and Big Data. FETLT 2016. Lecture Notes in Computer Science(), vol 10341. Springer, Cham. https://doi.org/10.1007/978-3-319-69365-1_9
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