- 著者
- Osamu KOBORI Yoko SAWAMIYA Naoki YOSHINAGA Angela C. ROWE Laura L. WILKINSON
- 出版者
- Psychologia Society
- 雑誌
- PSYCHOLOGIA (ISSN:00332852)
- 巻号頁・発行日
- vol.62, no.1, pp.63-76, 2020 (Released:2020-11-27)
- 参考文献数
- 44
- 被引用文献数
- 3
The aim of the present study was to examine the affect regulation strategies of college athletes using the novel diagrammatic ‘distance affect regulation mapping’ (DARM) tool. In a mixed-methods approach, 96 college athletes completed and reflected on the DARM and completed questionnaires measuring attachment orientation. The correlation analyses demonstrated that athletes who had secure attachment orientations were more likely to seek proximity to someone they relied on to help soothe stress. Qualitative analysis suggested that college athletes found the DARM helpful in highlighting the effective strategies they used to cope with stress. The DARM is a promising tool for researchers to visually capture a range of strategies, and for college athletes to reflect on, improve, and further develop their affect regulation strategies.
- 著者
- Wenliang Gao Nobuhiro Kaji Naoki Yoshinaga Masaru Kitsuregawa
- 出版者
- 一般社団法人 言語処理学会
- 雑誌
- 自然言語処理 (ISSN:13407619)
- 巻号頁・発行日
- vol.21, no.3, pp.541-561, 2014-06-16 (Released:2014-09-16)
- 参考文献数
- 20
- 被引用文献数
- 1 3
We propose a method of collective sentiment classification that assumes dependencies among labels of an input set of reviews. The key observation behind our method is that the distribution of polarity labels over reviews written by each user or written on each product is often skewed in the real world; intolerant users tend to report complaints while popular products are likely to receive praise. We encode these characteristics of users and products (referred to as user leniency and product popularity) by introducing global features in supervised learning. To resolve dependencies among labels of a given set of reviews, we explore two approximated decoding algorithms, “easiest-first decoding” and “two-stage decoding.” Experimental results on real-world datasets with user and/or product information confirm that our method contributed greatly to classification accuracy.