Letting Emotions Flow: Success Prediction by Modeling the Flow of Emotions in Books
Table 2: Mean (μ) and standard deviation (σ) for eight type of emotions for the last chunk.
['Dataset', '[BOLD] Anger  [ITALIC] μ', '[BOLD] Anger  [ITALIC] σ', '[BOLD] Anticipation  [ITALIC] μ', '[BOLD] Anticipation  [ITALIC] σ', '[BOLD] Disgust  [ITALIC] μ', '[BOLD] Disgust  [ITALIC] σ', '[BOLD] Fear  [ITALIC] μ', '[BOLD] Fear  [ITALIC] σ', '[BOLD] Joy  [ITALIC] μ', '[BOLD] Joy  [ITALIC] σ', '[BOLD] Sadness  [ITALIC] μ', '[BOLD] Sadness  [ITALIC] σ', '[BOLD] Surprise  [ITALIC] μ', '[BOLD] Surprise  [ITALIC] σ', '[BOLD] Trust  [ITALIC] μ', '[BOLD] Trust  [ITALIC] σ']
We see that authors generally end books with higher rates of positive words (μ=0.888) than negative words (μ=0.599) and the difference is significant (p<0.001). Similarly the means for anticipation, joy, trust, and fear are higher than for sadness, surprise, anger, and disgust. This further validates that authors prefer happy ending. Moving on to Successful and Unsuccessful categories, we see that the means for Successful books are higher than Unsuccessful books for anger, anticipation, disgust, fear, joy, and sadness (highly significant, p<0.001). We observe the same pattern for trust, and surprise, although the p value is only p<0.02 in this case. Moreover, the standard deviations for all emotions are significantly different across the two categories (p<0.001). Thus, emotion concentration (μ) and variation (σ) for Successful books are higher than for Unsuccessful books for all emotions in the NRC lexicon.