DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News
Table 7: Classification results – F1 measures
['[EMPTY]', '[ITALIC] DepecheMood  [ITALIC] f', '[ITALIC] DepecheMood  [ITALIC] nf', '[ITALIC] DepecheMood  [ITALIC] tfidf', '[ITALIC] bestse']
As a final test, we evaluate our resource in the classification task. The naïve approach used in this case consists in mapping the average of the scores of all words in the headline to a binary decision with fixed threshold at 0.5 for each emotion (after min-max normalization on all test headlines scores). For 3 emotions out of 5 we improve over the best performing systems, for one emotion we obtain the same results, and for one emotion we do not outperform other systems. In this case the difference in performances among the various ways of representing the word-by-document matrix is more prominent: normalized frequencies (nf) provide the best results.