Abstract page for arXiv paper 2611.08683: Compression of Emotion in Language Model Representations

Recognizing emotion in informal text at the granularity mental health support requires, telling nervousness from fear or remorse from sadness, remains far below usable accuracy: five years of work on ...

Multi-label emotion analysis engine for .NET 8. Powered by GoEmotions + RoBERTa (ONNX), with DirectML GPU/CPU support. (JP: .NET 8 用 多ラベル感情分析エンジン。GoEmotions / RoBERTa (ONNX) を使用し、DirectML GPU・CPU に...

Hmm… if the subject is social science, an absolute “right answer” is probably infinitely far away​, but putting that aside, I wonder if this could be improved quite a bit by stress-testing the classifier more aggressively? * * * From the public description alone, **BERTopic + a RoBERTa model trained on GoEmotions seems like a perfectly reasonable exploratory V1 stack** to me. I would not start by replacing either model. The place I would stress-test first is the measurement path between: text → classifier scores → emotion labels / probabilities → aggregation → post-vs-comment differences / temporal trends → substantive interpretation That seems especially important here because your questions are not only “does the classifier label individual documents reasonably?”, but also “do these labels support aggregate patterns across document types and time?” Those are related questions, but not quite the same one. ### If I wanted the highest-information checks for the least additional work My default order would probably be: 1. **Freeze and document the exact emotion inference contract** checkpoint/revision → label mapping → multi-label rule → thresholds → aggregation rule → denominator. 2. **Perturb the emotion measurement without changing the model** and see whether the headline findings survive: * threshold sensitivity; * thresholded labels vs probability aggregation, where both are meaningful; * post vs comment; * early/middle/late time slices; * short vs long/truncated text; * especially labels such as fear/nervousness if they carry much of the interpretation. 3. **Use a strong LLM as an independent second annotator on a stratified sample** , blinded to the GoEmotions outputs. 4. **Send only disagreements / unstable cases / headline-critical cases to a small blind human audit** if manual labeling is expensive. 5. **Only escalate to model replacement, recalibration, larger annotation, or more formal correction if one of those tests actually moves the conclusion.** So rather than making V1 much bigger immediately, I would use stress tests as a gate: if the findings are stable, stop; if something moves, that result tells you exactly where further work is worth spending time. There is also one scope distinction that I think could make the interpretation easier rather than weaker: observed Reddit text → model-detected / text-expressed emotion → climate-related affective discourse → climate-anxiety construct → individual psychological state → wider/youth population state V1 can potentially be quite useful on the left side without having to prove every bridge on the right side. I would treat those later bridges as separate validation problems rather than as reasons to discard the current analysis. Why I would stress-test the emotion measurement before changing the classifier (click for more details) A relatively low-HITL validation route using a strong LLM (click for more details) Post vs comment and temporal comparisons are the slices I would test first (click for more details) For BERTopic, I would test substantive robustness rather than search for a 'correct' parameter set (click for more details) A scope separation that might help with the climate-anxiety interpretation (click for more details) ### So, in compact form If the manuscript does not already contain equivalent checks, my first pass would be: 1. Keep BERTopic + GoEmotions. 2. Make the exact inference/aggregation rule reproducible. 3. Stress-test headline emotion results against reasonable thresholds/aggregation. 4. Check post/comment and early/middle/late slices separately. 5. Run a strong LLM as a blinded independent annotator on a stratified sample. 6. Human-review only a small blind sample, emphasizing disagreements/critical cases. 7. Perturb BERTopic just enough to test whether the substantive topic story survives. 8. Escalate only where one of those checks changes the conclusion. That seems to me like a relatively inexpensive way to get a lot more confidence in the current V1 **without turning it into a completely different project**. And importantly, a stress test does not only have value when it “passes.” If a headline result changes under one perturbation, that gives you a useful design boundary: stable under A/B/C sensitive to D which is much more actionable than a generic limitation. Since I have only seen the public project description rather than the manuscript/methods/code, I would phrase all of this conditionally — some or all of these controls may already be in the preprint. But given the three areas you explicitly asked about — modelling choices, emotion methodology, and interpretation — **the classifier-output → aggregate-result path is the first place I would spend validation effort before considering a larger redesign.**

Hi everyone, I’m Karim El-Sharkawy, the lead author on a research project with Sprout Climate Organization applying NLP methods to climate-related Reddit discourse. We’re preparing the work as a preprint and would really appreciate informal feedback from researchers working with NLP, transformers, topic modelling, or social media text. Our pipeline uses BERTopic for topic modelling and a RoBERTa-based GoEmotions model for multi-category emotion classification. We examine topic structure, emotional patterns, post/comment differences, and temporal changes in climate-related discussions. We’d appreciate your feedback on our modelling choices, emotion classification methodology, and interpretation of the results. If anyone is interested in taking a look, I’d be happy to send the manuscript privately. We’re hoping to use any feedback we receive either to strengthen the current V1 submission where possible, or to identify important methodological improvements and directions for subsequent versions of the work. If you provide substantial feedback that contributes to the manuscript, we’d also be happy to acknowledge your contribution in the paper, if you are comfortable with that. We’re also currently seeking an arXiv endorsement for the cs.SI (Social and Information Networks) category. If, after looking at the work, you feel comfortable endorsing the submission, we’d be extremely grateful. Thanks! Best regards, Karim El-Sharkawy ClimateLens Project Manager Sprout Climate Association

Fine-grained emotion classification, Goemotions corpus, Bert fine-tuning, Multi-label emotion detection, hyperparameter sensitivity analysis.

Fine-grained emotion detection, Goemotions dataset, BERT fine-tuning, Multi-label emotion classification, Hyperparameter optimization

Posted by Dana Alon and Jeongwoo Ko, Software Engineers, ...

This research investigates the integration of emotional diversity into Large Language Models (LLMs) to enhance collective intelligence. Inspired by the human wisdom of crowds phenomenon, where group decisions often outperform individual judgments, we fine-tuned the DarkIdol-Llama-3.1-8B model using Google's GoEmotions dataset and Low-Rank Adaptation (LoRA) to simulate emotionally diverse responses. Evaluating the model on a distance estimation task between Fargo, ND, and Seattle, WA, across 15,064 unique persona configurations, we analyzed how emotional states and social attributes influence decision-making. Our findings demonstrate that emotional integration shapes response patterns while maintaining acceptable prediction accuracy, revealing its potential to enhance artificial collective intelligence. This study provides valuable insights into the interplay of emotional diversity and decision-making in LLMs, suggesting pathways for creating emotionally aware AI systems that balance emotional depth with analytical precision.

Emotion detection in Brazilian Portuguese is less studied than in English. We benchmarked a large language model (Mistral 24B), a language-specific transformer model (BERTimbau), and the lexicon-based EmoAtlas for classifying emotions in Brazilian Portuguese text, with a focus on eight emotions derived from Plutchik’s model. Evaluation covered four corpora: 4000 stock-market tweets, 1000 news headlines, 5000 GoEmotions Reddit comments translated by LLMs, and 2000 DeepSeek-generated headlines. While BERTimbau achieved the highest average scores (accuracy 0.876, precision 0.529, and recall 0.423), an overlap with Mistral (accuracy 0.831, precision 0.522, and recall 0.539) and notable performance variability suggest there is no single top performer; however, both transformer-based models outperformed the lexicon-based EmoAtlas (accuracy 0.797) but required up to 40 times more computational resources. We also introduce a novel “emotional fingerprinting” methodology using a synthetically generated dataset to probe emotional alignment, which revealed an imperfect overlap in the emotional representations of the models. While LLMs deliver higher overall scores, EmoAtlas offers superior interpretability and efficiency, making it a cost-effective alternative. This work delivers the first quantitative benchmark for interpretable emotion detection in Brazilian Portuguese, with open datasets and code to foster research in multilingual natural language processing.