Abstract
A support vector classifier was compared to a lexicon-based approach for the task of detecting the stance categories speculation, contrast and conditional in English consumer reviews. Around 3,000 training instances were required to achieve a stable performance of an F-score of 90 for speculation. This outperformed the lexicon-based approach, for which an F-score of just above 80 was achieved. The machine learning results for the other two categories showed a lower average (an approximate F-score of 60 for contrast and 70 for conditional), as well as a larger variance, and were only slightly better than lexicon matching. Therefore, while machine learning was successful for detecting speculation, a well-curated lexicon might be a more suitable approach for detecting contrast and conditional.
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CITATION STYLE
Skeppstedt, M., Schamp-Bjerede, T., Sahlgren, M., Paradis, C., & Kerren, A. (2015). Detecting speculations, contrasts and conditionals in consumer reviews. In 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2015 at the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Proceedings (pp. 162–168). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/w15-2923
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