WRIME: A new dataset for emotional intensity estimation with subjective and objective annotations

Abstract

We annotate 17,000 SNS posts with both the writer’s subjective emotional intensity and the reader’s objective one to construct a Japanese emotion analysis dataset. In this study, we explore the difference between the emotional intensity of the writer and that of the readers with this dataset. We found that the reader cannot fully detect the emotions of the writer, especially anger and trust. In addition, experimental results in estimating the emotional intensity show that it is more difficult to estimate the writer’s subjective labels than the readers’. The large gap between the subjective and objective emotions imply the complexity of the mapping from a post to the subjective emotion intensities, which also leads to a lower performance with machine learning models.

Tomoyuki Kajiwara

Guest Assistant Professor

Natural Language Processing. Especially: Text Simplification, Paraphrasing, Semantic Textual Similarity, Quality Estimation.

Chenhui Chu

Guest Associate Professor

Noriko Takemura

Guest Associate Professor

She is working on ambient intelligence and gait recognition using pattern recognition and machine learning.

Yuta Nakashima

Professor

Yuta Nakashima is a professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.

Hajime Nagahara

Professor

He is working on computer vision and pattern recognition. His main research interests lie in image/video recognition and understanding, as well as applications of natural language processing techniques.