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