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    <abstract>Existing emotion datasets have supported a wide range of NLP tasks, but most are static resources that capture language use only at the time of their creation. As a result, they cannot represent how emotional meanings shift in response to cultural and social change. To address this limitation, we present NAIST LIFE STORY, a seven-year collection of Japanese emotion-related episodes that reflect contemporary topics across multiple years. Since 2017, 1,000 crowdsourced participants per quarter have written short texts describing personal experiences associated with seven emotions: anger, anxiety, disgust, trust, joy, sadness, and surprise. The dataset currently spans 28 periods and includes gender and age information for each participant. Analyses reveal systematic differences in text length and lexical diversity across emotions, as well as clear temporal trends linked to major events such as the COVID-19 pandemic. A preliminary experiment with a large language model shows that using this dataset as contextual evidence improves time-aware emotion inference, demonstrating its value for studying the evolving relationship between emotion and language.</abstract>
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%0 Conference Proceedings
%T NAIST LIFE STORY: A Seven-Year Crowdsourced Dataset of Japanese Emotion-related Episodes
%A Ito, Kazuhiro
%A Hayashi, Junko
%A Nagai, Hiroyuki
%A Wakamiya, Shoko
%A ARAMAKI, Eiji
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F ito-etal-2026-naist
%X Existing emotion datasets have supported a wide range of NLP tasks, but most are static resources that capture language use only at the time of their creation. As a result, they cannot represent how emotional meanings shift in response to cultural and social change. To address this limitation, we present NAIST LIFE STORY, a seven-year collection of Japanese emotion-related episodes that reflect contemporary topics across multiple years. Since 2017, 1,000 crowdsourced participants per quarter have written short texts describing personal experiences associated with seven emotions: anger, anxiety, disgust, trust, joy, sadness, and surprise. The dataset currently spans 28 periods and includes gender and age information for each participant. Analyses reveal systematic differences in text length and lexical diversity across emotions, as well as clear temporal trends linked to major events such as the COVID-19 pandemic. A preliminary experiment with a large language model shows that using this dataset as contextual evidence improves time-aware emotion inference, demonstrating its value for studying the evolving relationship between emotion and language.
%R 10.63317/3coh2p848v7q
%U https://aclanthology.org/2026.lrec-1.522/
%U https://doi.org/10.63317/3coh2p848v7q
%P 6573-6584

Markdown (Informal)

[NAIST LIFE STORY: A Seven-Year Crowdsourced Dataset of Japanese Emotion-related Episodes](https://aclanthology.org/2026.lrec-1.522/) (Ito et al., LREC 2026)

• NAIST LIFE STORY: A Seven-Year Crowdsourced Dataset of Japanese Emotion-related Episodes (Ito et al., LREC 2026)

ACL

• Kazuhiro Ito, Junko Hayashi, Hiroyuki Nagai, Shoko Wakamiya, and Eiji ARAMAKI. 2026. NAIST LIFE STORY: A Seven-Year Crowdsourced Dataset of Japanese Emotion-related Episodes. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6573–6584, Palma de Mallorca, Spain. ELRA Language Resource Association.