This study presents a difficulty-annotated corpus of Japanese newspaper articles. The annotation in our corpus has two key features. First, it is hierarchical, with difficulty levels labeled at the word, sentence, and document levels, respectively. Second, it is subjective, with 99 readers from diverse backgrounds labeling the difficulty levels based on their own perceptions. Detailed analysis of our corpus revealed correlations in difficulty levels across linguistic units, such as between words and sentences, and sentences and documents. We also found that text difficulty varies depending on reader attributes such as age and topics of interest. Furthermore, we trained Transformer encoders on this corpus and developed a personalized difficulty estimation model that takes into account both linguistic and attribute information. Experimental results demonstrate that incorporating reader attributes improves the performance of difficulty estimation and that multi-task learning across language units is beneficial. Our difficulty annotations are publicly available.

We propose a method for personalized emotional intensity estimation based on a writer’s personality test for Japanese SNS posts. Existing emotion analysis models are difficult to accurately estimate the writer’s subjective emotions behind the text. We personalize the emotion analysis using not only the text but also the writer’s personality information. Experimental results show that personality information improves the performance of emotional intensity estimation. Furthermore, a hybrid model combining the existing personalized method with ours achieved state-of-the-art performance.