Podlab at SemEval-2019 Task 3: The Importance of Being Shallow

2019-06-01 · SEMEVAL 2019 6 · Andrew Nguyen, Tobin South, Nigel Bean, Jonathan Tuke, Lewis Mitchell

This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92{\%} of the top score for the competition, demonstrating that {``}shallow{''} classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis.

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Classification Emotion Classification Feature Engineering General Classification

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