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.

Abstract. We reproduce three classification approaches with diverse feature sets for the task of classifying the sentiment expressed in a given tweet as either positive, neutral, or negative. The reproduced approaches are also combined in an ensemble, averaging the individual classifiers' confidence scores for the three classes and deciding sentiment polarity based on these averages. Our experimental evaluation on SemEval data shows our re-implementations to slightly outperform their respective originals. Moreover, in the SemEval Twitter sentiment detection tasks of 2013 and 2014, the ensemble of reproduced approaches would have been ranked in the top-5 among 50 participants. An error analysis shows that the ensemble classifier makes few severe misclassifications, such as identifying a positive sentiment in a negative tweet or vice versa. Instead, it tends to misclassify tweets as neutral that are not, which can be viewed as the safest option.