Can AI Read Our Emotions in Political Speech?

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Author: Denis Avetisyan

A new review assesses the potential-and limitations-of using advanced artificial intelligence to gauge emotional responses from video footage of political addresses.

Emotion intensity scores derived from video analysis by large language models correlate with human coders’ average ratings on the RAVDESS dataset, though reported correlation values differ from previous analyses due to a shift from bootstrapped averages to point estimates.
Emotion intensity scores derived from video analysis by large language models correlate with human coders’ average ratings on the RAVDESS dataset, though reported correlation values differ from previous analyses due to a shift from bootstrapped averages to point estimates.

This article systematically evaluates the performance of multimodal large language models in measuring emotional arousal from political speech videos, finding that while promising, their performance often lags behind simpler methods.

Despite growing interest in applying artificial intelligence to understand political communication, the effectiveness of multimodal large language models (mLLMs) for computational emotion analysis remains largely unproven. This paper, ‘Computational emotion analysis with multimodal LLMs: Current evidence on an emerging methodological opportunity’, systematically evaluates mLLMs’ ability to measure emotional arousal from video recordings of political speech, revealing that while promising under ideal conditions, their performance often falters with real-world parliamentary debates. Our findings suggest that mLLMs do not consistently outperform simpler methods, raising concerns about their reliability for downstream statistical inference. As generative AI rapidly evolves, can we develop robust evaluation frameworks to ensure these tools deliver meaningful insights into the complex dynamics of political emotion?

The Elusive Signal: Decoding the Nuances of Emotional Expression

The pursuit of genuinely intuitive human-computer interaction hinges on a machine’s ability to accurately decipher emotional states, a task proving remarkably difficult given the subtleties of real-world data. Current emotion recognition systems frequently falter when faced with the nuances of natural expression – a fleeting microexpression, a sarcastic tone, or the context-dependent meaning of a phrase. These systems often trained on curated datasets that don’t reflect the messy, uncontrolled conditions of everyday life, leading to diminished performance in practical applications. Consequently, despite advances in artificial intelligence, truly empathetic machines remain elusive, highlighting the urgent need for more robust and adaptable emotion recognition technologies that can navigate the inherent complexities of human communication and provide more seamless, natural interactions.

Emotional displays are rarely straightforward; a smile, for instance, can indicate happiness, politeness, or even sarcasm depending on the situation and the individual. This inherent ambiguity, coupled with significant variations in how people express – and interpret – emotions across cultures and personal experiences, presents a considerable challenge for automated systems. Consequently, effective emotion recognition requires analytical techniques that move beyond simplistic interpretations and account for contextual nuances, individual differences, and the subtle interplay of various expressive cues. These robust methods must be capable of disentangling genuine emotional signals from deceptive or masked expressions, achieving a more accurate and reliable assessment of affective states.

Emotion, as a human experience, rarely manifests through a single channel; instead, it’s a complex interplay of facial expressions, vocal tonality, body language, and linguistic content. Consequently, relying on unimodal data – analyzing text or audio in isolation – often yields incomplete and inaccurate emotion recognition. A smile, for example, might accompany sarcasm, completely altering the intended emotional message; a voice might tremble with fear or excitement, defying simple categorization. Studies demonstrate that integrating these multiple modalities – visual, auditory, and textual cues – significantly improves the accuracy and robustness of emotion detection systems. This multimodal approach allows algorithms to move beyond superficial analysis, capturing the nuanced and often contradictory signals inherent in genuine emotional expression, ultimately paving the way for more empathetic and effective human-computer interactions.

Emotion intensity scores derived from videos varied by emotion category, stimulus level, and whether assessed by human raters or a machine learning model.
Emotion intensity scores derived from videos varied by emotion category, stimulus level, and whether assessed by human raters or a machine learning model.

A Convergence of Streams: Multimodal LLMs and the Future of Affective Computing

Multimodal Large Language Models (LLMs) constitute a progression beyond traditional text-based models by integrating and processing data from multiple modalities, specifically text, visual inputs (images, video), and auditory signals (speech, sounds). This fusion allows for a more comprehensive understanding of emotional states, as emotions are rarely expressed solely through language. By analyzing facial expressions, body language, tone of voice, and linguistic content concurrently, these models aim to discern emotional nuances that would be inaccessible to unimodal systems. The ability to process these diverse data streams enables more accurate and contextually relevant emotion recognition, moving towards a more holistic interpretation of human emotional communication.

LLM In-Context Learning is a critical component in the operation of multimodal Large Language Models for emotion AI. This technique avoids the need for extensive fine-tuning by providing the LLM with a carefully constructed prompt containing examples of multimodal inputs paired with corresponding emotional labels. The model then leverages these examples to infer the emotional state associated with new, unseen inputs. Effective prompt engineering involves selecting representative examples, defining the desired output format, and potentially incorporating chain-of-thought reasoning to guide the model’s analysis of the combined textual, visual, and auditory data. The performance of the LLM is therefore directly dependent on the quality and relevance of the examples provided within the prompt, rather than inherent emotional understanding.

Gemini 2.5 Flash and Qwen 2.5 Omni currently represent state-of-the-art performance in multimodal emotion analysis, processing combined textual, visual, and auditory inputs to infer emotional states. Benchmarking indicates these models achieve high scores on standardized multimodal datasets; however, a critical limitation is the imperfect correlation between model predictions and human annotations of the same data. While capable of identifying emotional cues, the models do not consistently align with subjective human interpretation, suggesting potential biases or a lack of nuanced understanding in their emotional assessments. Current research focuses on improving this alignment through refined training data and architectural modifications.

Using the Cochrane et al. dataset, this comparison of Gemini 2.5 Flash and Qwen 2.5 models demonstrates that incorporating video data via few-shot in-context learning (ICL) generally improves sentiment scoring correlation and reduces root mean squared error compared to text-only ICL, with performance gains varying by model size and the number of ICL examples.
Using the Cochrane et al. dataset, this comparison of Gemini 2.5 Flash and Qwen 2.5 models demonstrates that incorporating video data via few-shot in-context learning (ICL) generally improves sentiment scoring correlation and reduces root mean squared error compared to text-only ICL, with performance gains varying by model size and the number of ICL examples.

The Imperative of Fidelity: Data Quality and Model Validation

The accuracy of emotion recognition systems is fundamentally constrained by the quality of the input data, specifically the Signal-to-Noise Ratio (SNR). Low SNR, resulting from factors like poor lighting, sensor limitations, or irrelevant background activity, introduces artifacts that obscure genuine emotional signals. Consequently, robust video pre-processing techniques are essential to mitigate noise and enhance the clarity of emotional cues. These techniques commonly include noise reduction filters, contrast normalization, facial landmark detection for region of interest extraction, and potentially, data augmentation strategies to improve model generalization. Insufficient pre-processing directly translates to reduced model performance and unreliable emotion classification, as the system struggles to differentiate between genuine expressions and spurious data variations.

While datasets such as RAVDESS and Cochrane et al. are frequently utilized for benchmarking emotion recognition models, their applicability to real-world performance is constrained by inherent limitations. These datasets typically feature actors portraying emotions in controlled laboratory settings, resulting in an overrepresentation of exaggerated expressions and a lack of natural variations found in spontaneous human behavior. The limited demographic diversity within these datasets-often focusing on Western cultures and specific age groups-further restricts their generalizability. Consequently, models trained exclusively on these benchmarks may exhibit reduced accuracy when processing data from unconstrained environments or diverse populations, necessitating the development and utilization of larger, more ecologically valid datasets for robust performance evaluation.

Accurate evaluation of emotion recognition models requires establishing a ground truth through rigorous human annotation, which serves as the benchmark against which model outputs are compared. Current multimodal Large Language Models (mLLMs) demonstrate a limited ability to align with human perception of emotional expression; performance metrics indicate a weak Pearson’s correlation ($r = 0.119$) when assessed on complex tasks. This low correlation suggests that while mLLMs can process multimodal data, their interpretation of nuanced emotional cues diverges significantly from human judgment, highlighting a critical area for improvement in affective computing.

Multimodal large language models demonstrate emotion intensity scoring performance, measured by correlation and root mean squared error against human ratings, that varies by speaker gender and is represented by average values with 90% confidence intervals, with TowerVideo-9B evaluated using 3-shot inference due to context window limitations.
Multimodal large language models demonstrate emotion intensity scoring performance, measured by correlation and root mean squared error against human ratings, that varies by speaker gender and is represented by average values with 90% confidence intervals, with TowerVideo-9B evaluated using 3-shot inference due to context window limitations.

Beyond Recognition: Towards a More Graceful Integration of Affective Computing

The accurate interpretation of human emotion hinges significantly on a model’s capacity to discern subtle expressive cues. Current emotion AI systems often struggle with the complexities of real-world expressions because they lack the architectural sophistication and training data needed to capture these nuances. Advancements in model architecture, such as exploring more complex neural network designs and attention mechanisms, are crucial for improving performance. Equally important is the development of robust training methodologies, including techniques like transfer learning and data augmentation, to enable models to generalize effectively from limited or noisy datasets. Future research must prioritize scaling model capacity-not simply increasing parameter counts, but optimizing the architecture to efficiently represent and process the intricate signals inherent in emotional expression-to move beyond superficial recognition and achieve a deeper understanding of human feeling.

Addressing the challenges of real-world emotion recognition necessitates a concentrated effort on enhancing data quality, particularly when dealing with the inherent noise present in unconstrained environments. Recent investigations into noise reduction techniques, however, have revealed a surprising limitation: while intuitively appealing, simply minimizing noise does not reliably translate to improved performance in emotion AI models. This suggests that the current limitations are not solely attributable to signal degradation, but rather stem from more complex issues surrounding data representation and model capacity. Future research should therefore move beyond isolated noise reduction strategies and explore holistic approaches encompassing improved data collection protocols, robust feature engineering, and the development of models capable of discerning genuine emotional signals amidst complex and varied background conditions. A deeper understanding of the types of noise impacting performance, as opposed to merely its overall level, will be crucial for achieving truly robust and reliable emotion AI systems.

The convergence of large language models with multimodal data streams – encompassing text, audio, and video – holds significant potential for transforming fields like mental health monitoring and personalized education by enabling more nuanced understanding of human emotional states. While initial explorations demonstrate promise, recent findings indicate that, currently, video data contributes limited incremental value to emotion recognition tasks; a strong correlation of 0.711 between textual analysis and human assessments of arousal suggests that emotional cues expressed through language often suffice. This highlights a critical area for future research: refining methods for effectively integrating and weighting multimodal inputs, and identifying the specific contexts where visual data provides genuinely unique insights beyond what can be gleaned from textual and auditory channels. Ultimately, unlocking the full potential of emotion AI requires a strategic focus on data fusion techniques and a realistic assessment of the contribution of each modality.

Performance of multi-modal large language models in scoring emotion intensity from video, assessed using correlation and root mean squared error against human ratings on the RAVDESS dataset, reveals speaker-gender-dependent variations with 90% confidence intervals indicated by black lines.
Performance of multi-modal large language models in scoring emotion intensity from video, assessed using correlation and root mean squared error against human ratings on the RAVDESS dataset, reveals speaker-gender-dependent variations with 90% confidence intervals indicated by black lines.

The systematic evaluation detailed within this study underscores a crucial point about technological advancement: initial promise doesn’t guarantee lasting efficacy. While multimodal large language models demonstrate potential in discerning emotional arousal from complex video data, their current limitations – often failing to outperform established, simpler methods – reveal a fundamental truth. As Linus Torvalds famously stated, “Talk is cheap. Show me the code.” This sentiment resonates strongly; the theoretical capabilities of these models must translate into demonstrable, practical improvements over existing techniques. The study’s findings suggest that the field is still grappling with the complexities of real-world application, proving that architecture without historical context – or, in this case, a solid baseline for comparison – remains fragile and ephemeral.

What Lies Ahead?

The pursuit of computational emotion analysis, particularly through the lens of multimodal large language models, reveals a familiar pattern. Initial enthusiasm encounters the persistent realities of system decay. These models, while exhibiting a certain surface-level competence, demonstrate that scaling complexity does not necessarily equate to deeper understanding. The observed limitations – a failure to consistently outperform simpler methods – are not failures of technology, but rather acknowledgements of inherent trade-offs. Each simplification introduced to facilitate computation carries a future cost, a debt accrued in the system’s memory.

Future work must move beyond simply demonstrating that these models can process multimodal data and begin to interrogate how they do so. Arousal scoring, as a proxy for emotional response, is a reduction of a profoundly complex phenomenon. The field risks building increasingly elaborate systems atop fragile foundations if it does not address the fundamental question of what is being measured, and whether that measurement truly reflects the intended construct. The focus should shift from chasing marginal gains in accuracy to understanding the inherent biases and limitations of the models themselves.

Ultimately, the value of this research lies not in the creation of a perfect emotion-reading machine, but in the insights it provides into the nature of communication and the challenges of artificial intelligence. The system will age, its performance will degrade, and new architectures will emerge. The question is not whether these models will be surpassed, but whether the knowledge gained during their development will prove resilient enough to inform the next iteration.

Original article: https://arxiv.org/pdf/2512.10882.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-13 08:29