Emotion Intensities in Tweets
Table 6: Pearson correlations on a subset of the test set where gold scores ≥0.5.
['[BOLD] anger  [ITALIC] Individual feature sets', '[BOLD] anger', '[BOLD] fear', '[BOLD] joy', '[BOLD] sad.', '[BOLD] avg.']
Firstly, the correlation scores are in general lower here in the 0.5 to 1 range of intensity scores than in the experiments over the full intensity range. This is simply because this is a harder task as now the systems do not benefit by making coarse distinctions over whether a tweet is in the lower range or in the higher range. Nonetheless, we observe that many of the broad patterns of results stay the same, with some differences. Lexicons still play a crucial role, however, now embeddings and word ngrams are not far behind. SentiStrength seems to be less useful in this range, suggesting that its main benefit was separating low- and high-intensity sadness words. NRC-Hash-Emo is still the source of the most predictive lexicon features.