Detecting Speculations, Contrasts and Conditionals in Consumer Reviews

Maria Skeppstedt, Teri Schamp-Bjerede, Magnus Sahlgren, Carita Paradis and Andreas Kerren

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. 

Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA '15)

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