Emotion Intensities in Tweets
Table 5: Pearson correlations (r) of emotion intensity predictions with gold scores. Best results for each column are shown in bold: highest score by a feature set, highest score using a single lexicon, and highest score using feature set combinations.
['[BOLD] anger  [ITALIC] Individual feature sets', '[BOLD] anger', '[BOLD] fear', '[BOLD] joy', '[BOLD] sad.', '[BOLD] avg.']
We developed our system by training on the official training sets and applying the learned models to the development sets. Once system parameters were frozen, the system trained on the combined training and development corpora. These models were applied to the official test sets. The last column ‘avg.’ shows the macro-average of the correlations for all of the emotions. Among the lexicons, NRC-Hash-Emo is the most predictive single lexicon. Lexicons that include Twitter-specific entries, lexicons that include intensity scores, and lexicons that label emotions and not just sentiment, tend to be more predictive on this task–dataset combination. NRC-Aff-Int has real-valued fine-grained word–emotion association scores for all the words in NRC-EmoLex that were marked as being associated with anger, fear, joy, and sadness. The correlations for anger, fear, and joy are similar (around 0.65), but the correlation for sadness is markedly higher (0.71). SentiStrength focuses solely on positive and negative classes, but provides numeric scores for each.