GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
Table 4: Pearson and Spearman correlation coefficients of baseline, character and word-level models and its ensemble for fear, anger, joy and sadness emotions and also average values. Results are calculated on the provided test set labels.
['Model', 'avg_p', 'avg_s', 'anger_p', 'anger_s', 'fear_p', 'fear_s', 'joy_p', 'joy_s', 'sad_p', 'sad_s']
The correlation rank coefficients assess how relevant and similar the two sets of ranking are. The character and word-level neural models achieve lower correlation values than the baseline, which is an indicator that models containing much of external knowledge perform better than end-to-end models on the tasks with a handful amount of samples; however, they bring additional value to the ensemble. Pearson and Spearman correlation coefficients are improved by 0.066 and 0.065 for the intensities in the full range of 0-1, achieving #4 position on the leaderboard. Additionally, the systems were evaluated on the sample with moderate or high emotional intensities with values from 0.5 to 1. Our ensemble model places rank #4 and shows 0.087 (∼ 18.5% relative) improvement on both correlation coefficients.