Introducing Aspects of Creativity in Automatic Poetry Generation
Table 2: Average percentage of correctly elicited emotion across four poems in each category
['[BOLD] Emotion', 'Anger', 'Antic.', 'Joy', 'Sad.', 'Trust']
In the first crowd-sourced analysis of our emotion-eliciting poetry we presented four poems from each category (of the five data-represented emotion categories) to ten human reviewers with undergraduate level educational backgrounds. All reviewers are native speakers of English. Poems presented were randomly selected from the top 20 EmoLex scored poems out of a pool of 1,000 generated poems. These reviewers were asked to rate each poem based on the emotions elicited within them after reading. An emotion was deemed correctly elicited if the associated Likert score was 4 or greater from the reviewer. When taking the average percentage of correct emotion-eliciting poems, the models of joy, sadness, and anger produced the most promising results while the trust and anticipation models were less than satisfactory. We believe this is because joy, sadness and anger are basic or fundamental emotions compared to trust and anticipation, which are more complex and difficult to explain. Although there are many opinions among psychologists about what constitute basic emotions, joy, sadness and anger, (especially the last two) seem to occur the most often in proposals that demarcate a set of basic emotions Ortony and Turner