@inproceedings{soni-etal-2026-semeval,
    title = "{S}em{E}val-2026 Task 2: Predicting Variation in Emotional Valence and Arousal over Time from Ecological Essays",
    author = "Soni, Nikita  and
      Schwartz, H. Andrew  and
      Boyd, Ryan  and
      Bui, Phi Long  and
      Mahwish, Syeda  and
      Nilsson, August  and
      V Ganesan, Adithya  and
      Ungar, Lyle  and
      Balasubramanian, Niranjan  and
      Mohammad, Saif",
    editor = "Kochmar, Ekaterina  and
      Ghosh, Debanjan  and
      North, Kai  and
      Komachi, Mamoru",
    booktitle = "Proceedings of the 20th {I}nternational {W}orkshop on {S}emantic {E}valuation (2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.semeval-1.451/",
    pages = "3731--3752",
    ISBN = "979-8-89176-414-9",
    abstract = "We present our shared task on predicting variation in emotional valence and arousal over time from ecological essays. The shared task uses a longitudinal dataset collected over 7 data collection phases of 14-day each spanning from 2021 to 2024, consisting of real-time essays and feeling words (e.g., happy, calm, sad, etc.) written in English by U.S. service-industry workers about ``how they are feeling''. Each text is associated with self-reported valence (V) (0 - 4, highly negative to highly positive affect) and arousal (A) (0 - 2, low to high energy) scores. The shared task consists of three parts, Subtask (1): Longitudinal Affect Assessment, Subtask (2): Forecasting Variation in Affect as a (2a): {\textbackslash}textit{\{}state change{\}}, and (2b): {\textbackslash}textit{\{}disposition change{\}}.The task attracted over 200 member registrations on Codabench, receiving official system submissions from 31 teams (total 104 team members), of which 28 teams (with 90 team members) submitted system description papers making it to our leaderboard. We discuss baseline results along with findings from 28 systems, highlighting the best-performing systems, a deeper analysis of performance on essays versus feeling words, and assessments for authors seen versus unseen during training. The datasets for this task are publicly available."
}