<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="noor-fatima-2026-emo">
    <titleInfo>
        <title>Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sadia</namePart>
        <namePart type="family">Noor</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mehwish</namePart>
        <namePart type="family">Fatima</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 20th International Workshop on Semantic Evaluation (2026)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ekaterina</namePart>
            <namePart type="family">Kochmar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kai</namePart>
            <namePart type="family">North</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Mamoru</namePart>
            <namePart type="family">Komachi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">San Diego, California, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-414-9</identifier>
    </relatedItem>
    <abstract>Modeling longitudinal affect requires capturing both stable user tendencies and transient textual signals. For SemEval-2026 Task 2, we propose the Trait-State Affect Forecaster (TSAF), which decomposes affect into persistent user traits and text-conditioned states integrated through adaptive gating. On per-text prediction (Subtask 1), TSAF achieves composite Pearson correlations of 0.645 for valence and 0.409 for arousal, outperforming the Linear(BERT) baseline. In forecasting tasks, results reveal strong short-term affective inertia, where prior affect dominates next-step prediction, while long-term drift remains challenging under sparse supervision; TSAF shows comparatively stronger gains for arousal in this setting. Analyses across user splits and modalities highlight the strengths and trade-offs of explicit trait-state modeling, particularly under cold-start and short-text conditions.</abstract>
    <identifier type="citekey">noor-fatima-2026-emo</identifier>
    <location>
        <url>https://aclanthology.org/2026.semeval-1.31/</url>
    </location>
    <part>
        <date>2026-07</date>
        <extent unit="page">
            <start>213</start>
            <end>220</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal
%A Noor, Sadia
%A Fatima, Mehwish
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F noor-fatima-2026-emo
%X Modeling longitudinal affect requires capturing both stable user tendencies and transient textual signals. For SemEval-2026 Task 2, we propose the Trait-State Affect Forecaster (TSAF), which decomposes affect into persistent user traits and text-conditioned states integrated through adaptive gating. On per-text prediction (Subtask 1), TSAF achieves composite Pearson correlations of 0.645 for valence and 0.409 for arousal, outperforming the Linear(BERT) baseline. In forecasting tasks, results reveal strong short-term affective inertia, where prior affect dominates next-step prediction, while long-term drift remains challenging under sparse supervision; TSAF shows comparatively stronger gains for arousal in this setting. Analyses across user splits and modalities highlight the strengths and trade-offs of explicit trait-state modeling, particularly under cold-start and short-text conditions.
%U https://aclanthology.org/2026.semeval-1.31/
%P 213-220

Markdown (Informal)

[Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal](https://aclanthology.org/2026.semeval-1.31/) (Noor & Fatima, SemEval 2026)

• Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal (Noor & Fatima, SemEval 2026)

ACL

• Sadia Noor and Mehwish Fatima. 2026. Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 213–220, San Diego, California, USA. Association for Computational Linguistics.