We reproduce four Twitter sentiment classification approaches that participated in previous SemEval editions with diverse feature sets. The reproduced approaches are combined in an ensemble, averaging the individual classifiers' confidence scores for the three classes (positive, neutral, negative) and deciding sentiment polarity based on these averages. The experimental evaluation on SemEval data shows our re-implementations to slightly outperform their respective originals. Moreover, not too surprisingly, the ensemble of the reproduced approaches serves as a strong baseline in the current edition where it is top-ranked on the 2015 test set.
The winner system REF combined in an ensemble four Twitter sentiment classification approaches that participated in previous SemEval editions.
Webis: An Ensemble for Twitter Sentiment Detection
SemEval@NAACL-HLT
Computer Science