Emotion Intensities in Tweets
Table 7: Emotion intensity transfer Pearson correlation on all target tweets.
['[BOLD] Train On', '[BOLD] Test On anger', '[BOLD] Test On fear', '[BOLD] Test On joy', '[BOLD] Test On sadness']
The numbers in the diagonal are results obtained using training and test data pertaining to the same emotion. These results are upperbound benchmarks for the non-diagonal results, which are expected to be lower. We observe that negative emotions are positively correlated with each other and negatively correlated with the only positive emotion (joy). The absolute values of these correlations go from r=0.23 to r=0.65. This shows that all of the emotion pairs are correlated at least to some extent, but that in some cases, for example, when learning from fear data and predicting sadness scores, one can obtain results (r=0.63) close to the upperbound benchmark (r=0.65). This means that even though one emotion may be strongly predictive of another, the predictive power need not be similar in the other direction. We also found that training on a simple combination of both the fear and sadness data and using the model to predict sadness obtained a correlation of 0.67 (exceeding the score obtained with just the sadness training set).