We propose a novel bootstrapping approach for the acquisition of lexicons from unannotated, informal online texts (in our case, Yelp reviews) for polar-opposite emotion dimension values from the Ortony/Clore/Collins model of emotions (e.g., desirable/undesirable). Our approach mitigates the intrinsic problem of limited supervision in bootstrapping with an effective strategy that softly labels unlabeled terms, which are then used to better estimate the quality of extraction patterns. Further, we propose multiple solutions to control for semantic drift by taking advantage of the polarity of the categories to be learned (e.g., praiseworthy vs. blameworthy). Experimental results demonstrate that our algorithm achieves considerably better performance than several baselines.
@InProceedings{HUANGFU18.95, author = {Luwen Huangfu and Mihai Surdeanu}, title = "{Bootstrapping Polar-Opposite Emotion Dimensions from Online Reviews}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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