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.

We annotate 35,000 SNS posts with both the writer’s subjective sentiment polarity labels and the reader’s objective ones to construct a Japanese sentiment analysis dataset. Our dataset includes intensity labels (none, weak, medium, and strong) for each of the eight basic emotions by Plutchik (joy, sadness, anticipation, surprise, anger, fear, disgust, and trust) as well as sentiment polarity labels (strong positive, positive, neutral, negative, and strong negative). Previous studies on emotion analysis have studied the analysis of basic emotions and sentiment polarity independently. In other words, there are few corpora that are annotated with both basic emotions and sentiment polarity. Our dataset is the first large-scale corpus to annotate both of these emotion labels, and from both the writer’s and reader’s perspectives. In this paper, we analyze the relationship between basic emotion intensity and sentiment polarity on our dataset and report the results of benchmarking sentiment polarity classification.