Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
Table 1: Weighted F1-scores for success classification in single task (ST) and multi task (MT) settings with varying chunk sequences when using all the book or only the first 1K sentences. ∗p<0.05 (McNemar significance test between Emotion Flow (chunks 50, MT, All) and NRC+SVM (chunk 10, MT, All))
['[BOLD] Book Content  [BOLD] Methods', '[BOLD] Chunks', '[BOLD] 1000 sents  [BOLD] ST', '[BOLD] 1000 sents  [BOLD] MT', '[BOLD] All  [BOLD] ST', '[BOLD] All  [BOLD] MT']
Our proposed method performs better than different baseline methods and obtains the highest weighted F1-score of 0.690. The results highlight the importance of taking into account the sequential flow of emotions across books to predict how much readers will like a book. We obtain better performance when we use an RNN to feed the sequences of emotion chunk vectors. The performance decreases with the SVM classifier, which discards this sequential information by treating each feature independently of each other. Moreover, increasing the granularity of the emotions by increasing the number of chunks seems to be helpful for success prediction. However, we see a slight decrease in performance beyond 50 chunks (weighted F1 score of 0.662 and 0.664 for 60 and 100 chunks, respectively).