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\title{Emo-tica at SemEval-2026 Task 2: Trait–State Affect Forecaster for Longitudinal Valence and Arousal}

\author{
Sadia Noor \and Mehwish Fatima \\
School of Electrical Engineering and Computer Science (SEECS) \\
National University of Sciences and Technology (NUST), Islamabad, Pakistan \\
\texttt{\{snoor.msds23seecs, mehwish.fatima\}@seecs.edu.pk}
}

\begin{document}
\maketitle

% ========================
\begin{abstract}
Modeling longitudinal affect requires capturing both stable user-level tendencies and transient textual fluctuations. For SemEval-2026 Task 2, we propose the \textbf{Trait-State Affect Forecaster (TSAF)}, which decomposes affect into persistent user traits and text-conditioned states combined via adaptive gating. On per-text prediction (Subtask 1), TSAF achieves composite Pearson correlations of 0.645 (valence) and 0.409 (arousal), substantially outperforming the official Linear(BERT) baseline. Forecasting results reveal strong short-term affective inertia, where prior affect dominates next-step prediction, while long-term dispositional drift remains challenging under limited user-level supervision, though TSAF achieves comparatively stronger arousal performance. Analyses across seen and unseen users and modalities highlight the strengths and trade-offs of explicit trait--state modeling for longitudinal affect prediction.
\end{abstract}

% ====================================================================================
Lets start with Introduction
\section{Introduction}
Dimensional affect modeling represents emotion along continuous valence and arousal axes grounded in psychological theory \citep{russell1980circumplex, bradley1994sam}. Transformer-based models achieve strong performance in text-based valence-arousal regression \citep{10.1007/978-3-031-28244-7_6}, but typically treat each text as an independent instance.

This assumption is restrictive in longitudinal settings, where affect evolves over time and varies systematically across individuals. In reflective writing and mental health contexts, emotional expression reflects both stable user-level tendencies and transient contextual fluctuations. While recent work emphasizes behavioral sequence modeling \citep{ganesan2026wordsequencesbehavioralsequences}, most approaches do not explicitly disentangle persistent baselines from dynamic textual signals.

SemEval-2026 Task 2 \citep{soni-etal-2026-semeval} formalizes this challenge through three subtasks: (1) per-text valence and arousal prediction, (2A) short-term state change forecasting, and (2B) long-term dispositional drift prediction under both seen and unseen user conditions. These settings require structured modeling of temporal dynamics alongside user-level personalization.

We propose the \textbf{Trait-State Affect Forecaster (TSAF)}, which decomposes affect into stable user traits and text-conditioned states. Learned user embeddings encode persistent baselines, contextual encoders capture momentary signals, and an adaptive gating mechanism balances both for each prediction. Across subtasks, this decomposition enables substantial gains in longitudinal affect estimation and structured forecasting approaches.

% ====================================================================================
\section{Related Work}
Prior work relevant to this task spans dimensional affect modeling, longitudinal sequence modeling, and personalization through trait–state representations.

\textbf{Dimensional Affect Modeling.} Emotion modeling in NLP increasingly adopts continuous valence and arousal (V\&A) representations grounded in psychological theory \cite{russell1980circumplex, bradley1994sam, 10.1007/978-3-031-28244-7_6, becker2026dimstancemultilingualdatasetsdimensional}. Early approaches rely on lexicons and shallow regression \cite{lin2024enhanced, mitsios-etal-2024-improved}, while transformer-based models achieve strong contextualized regression performance \cite{ahire2025mavenmultimodalattentionvalencearousal}. However, most systems treat texts independently and ignore longitudinal structure.

\textbf{Longitudinal Modeling and Forecasting.} Recent work shifts from isolated text modeling to behavioral sequence modeling \cite{ganesan2026wordsequencesbehavioralsequences}. Temporal architectures, including hierarchical transformers and change-detection models, track evolving mood trajectories \cite{tseriotou-etal-2023-sequential, hills-etal-2024-exciting}. Forecasting remains difficult due to affective inertia and user heterogeneity \cite{ganesan2026wordsequencesbehavioralsequences, tripodi-etal-2025-assessing}. Many methods rely on autoregressive or population-level assumptions \cite{TOMMASEL2021259, matero-schwartz-2020-autoregressive}, limiting personalization.

\textbf{Personalization and Trait–State Modeling.} Psychological theory distinguishes stable traits from transient states \cite{Steyer1999, Wurpts2015}. NLP personalization typically incorporates user embeddings or identifiers \cite{mireshghallah-etal-2022-useridentifier, soni-etal-2025-evaluation, shu2024enhanced}, but often entangles persistent baselines with contextual signals \cite{Harry2026StateBlind}. In contrast, we explicitly separate trait and state components and combine them via adaptive gating, enabling structured modeling of both within-person and between-person variance \cite{Schuurman2023WithinBetween, Hoffman2009PersonsAsContexts}.


% ====================================================================================
\section{Dataset and Analysis}\label{Dataset and Analysis}
The SemEval-2026 Task 2 training set contains 2,764 longitudinal texts from 137 U.S. service-industry workers, collected between March 2021 and December 2024 across seven phases. Each instance is annotated with valence $[-2, 2]$ and arousal $[0, 2]$, and includes a user ID, timestamp, phase label, and a modality flag (\texttt{is\_words}) distinguishing feeling-word entries from essays.

\textbf{User Heterogeneity.} Texts per user range from 2 to 206 (mean 20.2), indicating strong participation imbalance. Per-user valence standard deviation ranges from 0 to 2.08 (mean 1.05), while arousal shows lower dispersion (mean 0.67). Figures~\ref{fig:valence_violin} and~\ref{fig:arousal_violin} illustrate substantial between-user differences in affective range and variability.

\begin{figure}[!ht]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/valence_violin.pdf}
    \caption{Per-user valence distributions across the five most active users. The substantial heterogeneity in affect range and variance highlights individual differences and motivates explicit modeling of stable user-level traits.}
    \label{fig:valence_violin}
\end{figure}

\begin{figure}[!ht]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/arousal_violin.pdf}
    \caption{Per-user arousal distributions across the five most active users. Compared to valence, arousal exhibits lower overall dispersion but persistent between-user variability, supporting trait-based modeling.}
    \label{fig:arousal_violin}
\end{figure}

\textbf{Bimodal Text Structure.} The dataset comprises 1,433 feeling-word entries and 1,331 essays. Feeling-word entries cluster around 8–10 words, while essays extend up to 225 words (75th percentile: 52), making them roughly 6-7$\times$ longer. These modalities differ substantially in expressive density, motivating modality-aware modeling.

\textbf{Temporal Sparsity.} Data are collected in discrete waves, with activity concentrated in later phases (6-7) and limited coverage earlier. This irregular sampling requires leakage-free temporal features and chronological splits that preserve natural affective trajectories. The distributions of step-wise affect changes for Subtask 2A are shown in Figures~\ref{fig:statechange_v} and~\ref{fig:statechange_a}.

\begin{figure}[!ht]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/statechange_valence.pdf}
    \caption{Distribution of step-wise valence changes (Subtask 2A). The strong concentration near $\Delta = 0$ reveals pronounced short-term affective inertia, explaining the competitiveness of autoregressive baselines.}
    \label{fig:statechange_v}
\end{figure}

\begin{figure}[!ht]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/statechange_arousal.pdf}
    \caption{Distribution of step-wise arousal changes (Subtask 2A), similarly centered near $\Delta = 0$, confirming inertia in short-term activation dynamics.}
    \label{fig:statechange_a}
\end{figure}


Additional visualizations are provided in Appendix~\ref{app:eda}.

% ====================================================================================
\begin{figure*}[t]
    \centering
    \includegraphics[width=\textwidth]{latex/Figures/diagram.drawio.pdf}
    \caption{Trait-State Affect Forecaster (TSAF). Texts are encoded with DistilBERT to produce state representations, while learned user embeddings encode stable trait baselines. An adaptive gate computes a weighted combination of trait and state predictions to predict valence and arousal.}
    \label{fig:trait_state_architecture}
\end{figure*}
% ====================================================================================
\section{Trait-State Affect Forecaster (TSAF)}
Figure~\ref{fig:trait_state_architecture} illustrates our proposed \textbf{Trait-State Affect Forecaster (TSAF)}. The model decomposes affect prediction into two complementary components: a \emph{trait} module capturing stable user-level baselines and a \emph{state} module modeling text-dependent fluctuations. This decomposition reflects the dataset's strong between-user variability and within-user dynamics. TSAF models affect as a gated interaction between persistent dispositions and contextual signals. The full gated architecture is used for Subtask~1, while Subtasks~2A and~2B use lightweight models informed by the same trait–state decomposition. Implementation details and code are publicly available.\footnote{\url{https://github.com/Sadia-Noor/Emo-tica_SemEval-2026-task_2}}

\subsection{Subtask 1: Trait-State Affect Modeling}
Given user $u$ and text $x_{u,t}$, TSAF predicts valence and arousal as a gated mixture of trait and state signals.

\textbf{State Representation.} Each text is encoded using \texttt{DistilBERT-base-uncased} with masked mean pooling, yielding $\mathbf{h}_{u,t} = \text{Encoder}(x_{u,t}) \in \mathbb{R}^{d}$.

\textbf{Trait Representation.} Each user $u$ is assigned a learned embedding $\mathbf{z}_u \in \mathbb{R}^{k}$, trained jointly to capture stable affective baselines. Unseen users share a common \texttt{UNK} embedding.

\textbf{Regression Heads.} Separate linear heads produce affect estimates $\hat{\mathbf{y}}^{\text{state}}_{u,t} = f_{\text{state}}(\mathbf{h}_{u,t})$ and $\hat{\mathbf{y}}^{\text{trait}}_{u} = f_{\text{trait}}(\mathbf{z}_u)$, where $\hat{\mathbf{y}} \in \mathbb{R}^{2}$ denotes valence and arousal.

\textbf{Adaptive Gating.} TSAF learns a gate $g_{u,t} = \sigma\!\left(W_g [\mathbf{h}_{u,t}; \mathbf{z}_u] + b_g \right)$, where $g_{u,t} \in (0,1)$ is a scalar gating coefficient. The final prediction is computed as $\hat{\mathbf{y}}_{u,t} = g_{u,t}\hat{\mathbf{y}}^{\text{state}}_{u,t} + (1 - g_{u,t})\hat{\mathbf{y}}^{\text{trait}}_{u}$, dynamically balancing user baselines and contextual cues.

\textbf{Training.} We minimize mean squared error $\mathcal{L} = \frac{1}{N} \sum_{u,t} \|\hat{\mathbf{y}}_{u,t} - \mathbf{y}_{u,t}\|_2^2$ and select models using the official composite Pearson correlation.

\subsection{Subtask 2A: State Change Forecasting}
For each user at time $t$, TSAF predicts next-step affect change $\Delta^{\text{state}}_{u,t}$ using strictly past information. Features include current affect $(v_{u,t}, a_{u,t})$, first-order differences, rolling statistics (window=3), time gaps, phase indicators, and modality. We train Ridge regression for valence and LightGBM for arousal. Users without history default to the global mean change.

\subsection{Subtask 2B: Dispositional Change Modeling}
To capture long-term affect drift, TSAF aggregates first-half user entries into summary statistics: mean, standard deviation, extrema, linear trend, entry count, and temporal span. Separate Ridge regressors predict valence ($\alpha=100$) and arousal ($\alpha=1$), with stronger regularization for valence to mitigate overfitting under limited user-level data.


% ====================================================================================
\section{Experiments}
\subsection{Data Splits}\label{sec:datasplits}
For Subtask 1, we exclude users with fewer than six entries to ensure reliable within-user correlation estimates. From the remaining users, 15\% are held out entirely as unseen users. For seen users, the earliest 80\% of texts are used for training and the most recent 20\% for validation, preserving chronological order.

For Subtask 2A, we adopt a last-step-per-user strategy, reserving each user's final labeled transition for validation. For Subtask 2B, users are split randomly in an 80/20 ratio; final models are retrained on all labeled users prior to test prediction.

\subsection{Implementation Details}
Texts are tokenized with \texttt{DistilBERT-base-uncased} (maximum 256 tokens) without additional normalization and sorted chronologically per user. All temporal features for Subtasks 2A and 2B are computed strictly from past observations to prevent leakage. Neural components are implemented in PyTorch using HuggingFace Transformers. The user embedding dimension is 64, and the regression head hidden size is 256, with a reserved \texttt{UNK} index for unseen users. Structured models use \texttt{scikit-learn} and \texttt{LightGBM}.

\subsection{Training and Evaluation}
For Subtask 1, we minimize MSE using AdamW (learning rate $2{\times}10^{-5}$, batch size 16) for up to 20 epochs with early stopping (patience 3). Model selection is based on composite Pearson $r$ on the validation set. For Subtask 2A, Ridge regression (cross-validated $\alpha$) models valence change, and LightGBM (300 boosting rounds, learning rate $0.05$) models arousal change. For Subtask 2B, Ridge regularization is selected via grid search over $\alpha \in \{0.01, 0.1, 1, 10, 100\}$, yielding $\alpha=100$ for valence and $\alpha=1$ for arousal. We follow the official SemEval-2026 Task 2 evaluation protocol, reporting Pearson $r$ and MAE for all subtasks. For Subtask 1, we additionally report within-person and between-person Pearson $r$, combined into a composite score via Fisher-$z$ averaging.

%====================================================================================
\section{Results}
Tables~\ref{tab:subtask1_results} and~\ref{tab:subtask2_results} report official test results. We focus on correlation-based metrics, as ranking is determined by Pearson $r$. We report results for the official shared-task baselines Linear(BERT), Linear(BERT; previous), linear(prev), rand(mean), and rand(zero), which we denote as L-BERT, L-BERT+Prev, L-Prev, Rand-M, and Rand-Z, respectively.

\begin{table}[t]
\centering
\small
\begin{tabular}{lccc}
\toprule
Model & Comp & Between & Within \\
\midrule

\multicolumn{4}{c}{\textbf{Valence ($r$)}}\\
\midrule
L-BERT & .557 & .659 & .435 \\
Rand-M & .000 & .028 & .000 \\
\textbf{TSAF} & \textbf{.645} & \textbf{.705} & \textbf{.577} \\

\midrule
\multicolumn{4}{c}{\textbf{Valence (MAE)}} \\
\midrule
L-BERT & .743 & .472 & .886 \\
Rand-M & .000 & .627 & 1.041 \\
\textbf{TSAF} & \textbf{.685} & \textbf{.472} & \textbf{.822} \\

\midrule
\multicolumn{4}{c}{\textbf{Arousal ($r$)}} \\
\midrule
L-BERT & .299 & .343 & .253 \\
Rand-M & .000 & .096 & .000 \\
\textbf{TSAF} & \textbf{.409} & \textbf{.430} & \textbf{.388} \\

\midrule
\multicolumn{4}{c}{\textbf{Arousal (MAE)}} \\
\midrule
L-BERT & .459 & .311 & .585 \\
Rand-M & .488 & .326 & .622 \\
\textbf{TSAF} & \textbf{.407} & \textbf{.275} & \textbf{.524} \\

\bottomrule
\end{tabular}
\caption{Official Subtask 1 test performance (Pearson $r$ and MAE).}
% \caption{Official Subtask 1 test performance. TSAF improves composite Pearson correlation over L-BERT for both valence and arousal, with consistent gains in between- and within-user alignment while also reducing mean absolute error across both affect dimensions.}
\label{tab:subtask1_results}
\end{table}

\textbf{Subtask 1: Longitudinal Affect Modeling.} 
TSAF ranks 15th of 27 teams, achieving composite $r=0.645$ for valence and $r=0.409$ for arousal. 
Relative to L-BERT, TSAF improves composite correlation by +0.088 (valence) and +0.110 (arousal). Gains are consistent across both between-user and within-user correlations, indicating improved modeling of stable user baselines as well as text-level fluctuations. The improvements are particularly pronounced for within-user alignment, supporting the effectiveness of the trait–state decomposition for longitudinal affect estimation. A detailed breakdown across seen and unseen users and text modalities is provided in Appendix~\ref{app:additional}.

\begin{table}[t]
\centering
\small
\begin{tabular}{lcccc}
\toprule
Model & $r_V$ & $r_A$ & MAE$_V$ & MAE$_A$ \\
\midrule

\multicolumn{5}{c}{\textbf{Subtask 2A: State Change}} \\
\midrule
L-Prev& \textbf{.615} & \textbf{.670} & \textbf{1.168} & \textbf{.638} \\
L-BERT+Prev & .430 & .405 & 1.251 & .708 \\
Rand-Z & .000 & .000 & 1.261 & .696 \\
\textbf{TSAF} & .424 & .355 & 1.297 & .842 \\

\midrule
\multicolumn{5}{c}{\textbf{Subtask 2B: Disposition Change}} \\
\midrule
L-Prev& \textbf{.434} & \textbf{.584} & \textbf{.406} & \textbf{.286} \\
L-BERT+Prev & -.029 & .019 & .436 & .305 \\
Rand-Z & .000 & .000 & .417 & .296 \\
\textbf{TSAF} & .257 & .418 & .461 & .298 \\

\bottomrule
\end{tabular}
\caption{Official Subtask 2 forecasting performance (Pearson $r$ and MAE).}
% \caption{Official Subtask 2 forecasting performance. L-Prev dominates short-term state change due to affective inertia, while TSAF shows comparatively stronger performance for long-term dispositional drift, particularly for arousal.}
\label{tab:subtask2_results}
\end{table}

\textbf{Subtask 2A: Short-Term State Forecasting.} 
TSAF ranks 11th of 19 teams with $r=0.424$ (valence) and $r=0.355$ (arousal). Although it exceeds the Rand-Z baseline, it underperforms L-Prev, where immediate prior affect alone achieves $r=0.615$ and $r=0.670$. This highlights strong short-term affective inertia: recent affect dominates next-step transitions, limiting the benefit of more expressive modeling.

\textbf{Subtask 2B: Dispositional Drift Prediction.} 
For long-term drift, TSAF obtains $r=0.257$ (valence) and $r=0.418$ (arousal), ranking 3rd for arousal. Unlike Subtask 2A, short-term autoregressive signals are insufficient for long-range prediction, and aggregated user-level statistics become more informative. The stronger arousal performance suggests that activation trends are more structurally recoverable than valence drift under limited user-level supervision.

%------------------------------------
\subsection{Validation Ablation Analysis} 
On the validation split (Section~\ref{sec:datasplits}), we compare (i) state-only, (ii) concatenation of user and text embeddings, and (iii) gated fusion (TSAF). The state-only model already performs strongly, confirming that contextual text representations carry the dominant predictive signal. Simple concatenation yields the highest peak validation correlation but increases the seen–unseen performance gap, indicating reliance on user-specific parameters. Gated fusion achieves slightly lower peak correlation but improved robustness, balancing personalization and generalization. Overall, affect prediction in this dataset is primarily state-driven, with trait modeling providing complementary, regularized gains.

%------------------------------------
\subsection{Error Analysis}
We analyze validation errors to characterize TSAF's inductive biases and failure modes.

\textbf{Overall Difficulty.} MAE is 0.77 for valence and 0.48 for arousal, confirming that valence is substantially harder to predict. The higher variance and broader label range for valence likely increase regression difficulty.

\textbf{Extreme Valence Underestimation.} For strongly negative instances ($v < -1.5$, $n=141$), the model exhibits a mean positive bias of +1.01, indicating systematic underestimation of extreme negativity. This reflects regression toward user-level baselines, suggesting that trait components regularize predictions but attenuate high-magnitude affect.

\textbf{Affective Inertia and Spikes.} For large arousal shifts ($|\Delta a| > 1.0$), MAE rises to 0.69 compared to the overall 0.48. Abrupt transitions are therefore disproportionately difficult, consistent with strong affective inertia observed in Subtask~2A. The model captures gradual trends more reliably than sharp state changes.

\textbf{Polarity Reversals.} High-error cases frequently involve polarity reversals, where strongly positive or negative labels are predicted with opposite sign. These instances often occur in longer essays containing mixed or contrastive emotional cues. In such cases, stable trait signals may dominate localized textual indicators, leading to misalignment.

Overall, errors reveal a structural trade-off: trait modeling improves global user alignment and regularization, but can reduce responsiveness to extreme or rapidly changing affective states.

%==========================================================================================
\section{Conclusions}
We introduce the Trait-State Affect Forecaster (TSAF) for longitudinal valence and arousal modeling in SemEval-2026 Task 2. By decomposing affect into stable user baselines and text-conditioned signals combined via adaptive gating, TSAF improves over the official Linear(BERT) baseline on per-text prediction. Our analysis shows that personalization yields consistent gains, although the dominant signal remains text-driven. User embeddings provide notable benefits under cold-start conditions, and arousal aligns more strongly with stable user tendencies than valence. Forecasting remains challenging due to strong short-term inertia and limited supervision for long-term drift. Overall, structured personalization strengthens longitudinal affect modeling, while richer sequence-aware architectures are needed to better capture dynamic trajectories.

%=====================
\section*{Limitations}
Despite improvements over the official baseline in Subtask~1, limitations remain. For Subtask~1, personalization relies on learned user embeddings; unseen users share a single \texttt{UNK} representation, limiting adaptation to new individuals. Moreover, predictions are made independently per text and do not explicitly incorporate short-term affect history. For Subtask~2A, we rely on engineered temporal features rather than explicit sequence or autoregressive modeling, which limits modeling of fine-grained emotional transitions. For Subtask~2B, user trajectories are summarized with simple aggregates under limited data. Overall, while the trait-state framework strengthens longitudinal affect assessment, forecasting relies on relatively simple temporal modeling, leaving room for more expressive sequence-aware approaches.

%===========================


% \section*{Acknowledgments}
\bibliography{custom}

\appendix
\section{Dataset Analysis Figures}\label{app:eda}

This appendix provides supplementary visualizations supporting the dataset analysis in Section~\ref{Dataset and Analysis}. The figures illustrate temporal collection patterns, per-user variability in valence and arousal, step-wise state-change distributions, and modality coverage in the valence--arousal space.

\begin{figure}[h!]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/monthly_activity.pdf}
    \caption{Monthly distribution of training texts across the full data collection period. Activity is highly uneven, with substantial increases in late 2024, indicating wave-based rather than continuous data collection. This temporal imbalance introduces periods of sparsity and density that are relevant for longitudinal sequence modeling.}
    \label{fig:monthly}
\end{figure}


\begin{figure}[h!]
    \centering
    \includegraphics[width=\columnwidth]{latex/Figures/va_scatter.pdf}
    \caption{Valence–arousal coverage by modality (essays vs.\ feeling-word entries). Both modalities occupy similar affective regions despite large differences in text length, suggesting differences stem from expressive density rather than label distribution.}
    \label{fig:scatter}
\end{figure}

\section{Additional Subtask 1 Analysis}\label{app:additional}

Tables~\ref{tab:valence_additional} and~\ref{tab:arousal_additional} provide a breakdown of Subtask~1 performance across user splits and text modalities. We compare TSAF and L-BERT using composite, between-user, and within-user Pearson correlations, along with corresponding MAE metrics.

\textbf{Seen Users.}  
The baseline achieves higher valence correlations, particularly for the between-user component, indicating that contextual embeddings already capture substantial stable variance. TSAF maintains comparable within-user correlations while showing stronger generalization under unseen-user settings.

\textbf{Unseen Users.}  
TSAF improves composite correlations for both valence and arousal, with particularly large gains for arousal. This suggests that the shared trait embedding and adaptive gating improve robustness under cold-start conditions.

\textbf{Modality Analysis.}  
For feeling-word entries, TSAF consistently outperforms the baseline across both affect dimensions. The concise nature of these texts benefits from adaptive balancing between trait and state components. For essays, the baseline shows stronger between-user correlations, suggesting that longer narratives already encode stable affective signals without requiring explicit personalization.

Overall, personalization yields the greatest benefit under cold-start settings and for short affective expressions, while simpler linear models remain competitive for modeling broad between-user variance in longer texts.

\begin{table}[ht!]
\centering
\small
\begin{tabular}{lcc}
\toprule
 & L-BERT & TSAF \\
\midrule

\multicolumn{3}{c}{\textbf{Valence Correlation}} \\
\midrule
\textit{$r_{comp}$} \\
Seen Users   & .748 & .658 \\
Unseen Users & .562 & \textbf{.640} \\
Essay Only   & .546 & \textbf{.602} \\
Words Only   & .470 & \textbf{.669} \\

\midrule
\textit{$r_{between}$} \\
Seen Users   & .851 & .732 \\
Unseen Users & .624 & \textbf{.691} \\
Essay Only   & .629 & \textbf{.652} \\
Words Only   & .570 & \textbf{.738} \\

\midrule
\textit{$r_{within}$} \\
Seen Users   & .588 & .569 \\
Unseen Users & .494 & \textbf{.583} \\
Essay Only   & .451 & \textbf{.547} \\
Words Only   & .357 & \textbf{.586} \\

\midrule
\multicolumn{3}{c}{\textbf{Valence MAE}} \\
\midrule
\textit{$MAE_{comp}$} \\
Seen Users   & \textbf{.598} & .710 \\
Unseen Users & .772 & \textbf{.660} \\
Essay Only   & \textbf{.710} & .745 \\
Words Only   & .823 & \textbf{.603} \\

\midrule
\textit{$MAE_{between}$} \\
Seen Users   & \textbf{.354} & .543 \\
Unseen Users & .411 & \textbf{.399} \\
Essay Only   & \textbf{.548} & .563 \\
Words Only   & .641 & \textbf{.419} \\

\midrule
\textit{$MAE_{within}$} \\
Seen Users   & \textbf{.766} & .823 \\
Unseen Users & .924 & \textbf{.822} \\
Essay Only   & \textbf{.820} & .858 \\
Words Only   & .918 & \textbf{.739} \\

\bottomrule
\end{tabular}
\caption{Subtask 1 Valence performance across user splits and modalities.}
\label{tab:valence_additional}
\end{table}


\begin{table}[ht!]
\centering
\small
\begin{tabular}{lcc}
\toprule
 & L-BERT & TSAF \\
\midrule

\multicolumn{3}{c}{\textbf{Arousal Correlation}} \\
\midrule
\textit{$r_{comp}$} \\
Seen Users   & .422 & .332 \\
Unseen Users & .238 & \textbf{.512} \\
Essay Only   & .395 & .313 \\
Words Only   & .158 & \textbf{.582} \\

\midrule
\textit{$r_{between}$} \\
Seen Users   & .592 & .383 \\
Unseen Users & .258 & \textbf{.540} \\
Essay Only   & .395 & .296 \\
Words Only   & .124 & \textbf{.638} \\

\midrule
\textit{$r_{within}$} \\
Seen Users   & .215 & \textbf{.278} \\
Unseen Users & .238 & \textbf{.483} \\
Essay Only   & .395 & .330 \\
Words Only   & .191 & \textbf{.520} \\

\midrule
\multicolumn{3}{c}{\textbf{Arousal MAE}} \\
\midrule
\textit{$MAE_{comp}$} \\
Seen Users   & \textbf{.408} & .415 \\
Unseen Users & .488 & \textbf{.398} \\
Essay Only   & .468 & \textbf{.437} \\
Words Only   & .487 & \textbf{.389} \\

\midrule
\textit{$MAE_{between}$} \\
Seen Users   & \textbf{.268} & .288 \\
Unseen Users & .309 & \textbf{.261} \\
Essay Only   & .358 & \textbf{.298} \\
Words Only   & .384 & \textbf{.292} \\

\midrule
\textit{$MAE_{within}$} \\
Seen Users   & .531 & \textbf{.529} \\
Unseen Users & .633 & \textbf{.519} \\
Essay Only   & .566 & \textbf{.558} \\
Words Only   & .578 & \textbf{.479} \\

\bottomrule
\end{tabular}
\caption{Subtask 1 Arousal performance across user splits and modalities.}
\label{tab:arousal_additional}
\end{table}


\end{document}
