Rule-based Emotion Detection on Social Media:Putting Tweets on Plutchik’s Wheel
Table 2: Accuracies on the Twitter dataset.
['Language', 'Majority', 'SVM W.C.', 'SVM TF-IDF', 'Regr W.C.', 'Regr TF-IDF', 'RAE', '[BOLD] RBEM-Emo']
For each classifier, we report the accuracy on each language (being Dutch, English and German) and report a total accuracy which is the average accuracy over all messages in all three languages. A generic result over all classifiers is that the accuracies on English data seem to be the lowest, implying most ambiguity within this language. Remarkable is that the recursive auto-encoder performs worse than SVM and regression models and yields no benefit over the majority class guess. Again, this could be due to the small size of the corpus or difficulty in finding the most suitable model parameters. There is no clear evidence on whether TF-IDF scores or word counts work better for this dataset. The RBEM-Emo classifiers yields the highest accuracy for each of three languages.