Models predict emotional valence from narratives, matching human raters.
problem Predicting emotional valence from multimodal time-series data.
method Adapted attention-based mechanisms (Transformer, Memory Fusion Network) to emotional narratives.
result Models perform well, matching human raters on emotional valence prediction.
Automated prediction of valence, one key feature of a person's emotional state, from individuals' personal narratives may provide crucial information for mental healthcare (e.g. early diagnosis of mental diseases, supervision of disease course, etc.). In the Interspeech 2018 ComParE Self-Assessed Affect challenge, the …
Study finds strong link between crypto narratives and prices.
problem Understanding the impact of crypto narratives on prices.
method Topic modeling of Twitter data combined with sentiment analysis.
result Strong correlation between narratives and crypto prices.
Platform combines RL and language models to study narrative influence on AI decisions.
problem Understanding how narrative elements shape AI decision-making.
method Dual-system architecture with reinforcement learning and language model integration.
result Initial experiments show narrative frameworks can influence AI decision-making.
Improved earnings predictions through text-morphed earnings calls.
problem Improving earnings prediction models using narrative information.
method Introducing a text-morphing methodology to generate counterfactual transcripts.
result Analysts over-react to sentiment and under-react to risk and uncertainty.
Algorithm improves SLR efficiency in financial narratives.
problem Fragmented understanding of financial narratives.
method NLP, clustering, interpretability tools.
result Unified narrative modeling approaches needed.
A new platform models how narratives influence financial markets.
problem Explaining irrational market behaviors through narratives.
method Integrated opinion dynamics and agent-based modeling.
result Initial results show how narratives shape financial outcomes.
Automatic understanding of domain specific texts in order to extract useful relationships for later use is a non-trivial task. One such relationship would be between railroad accidents' causes and their correspondent descriptions in reports. From 2001 to 2016 rail accidents in the U.S. cost more than $4.6B. Railroads i…
Emotional content is a crucial ingredient in user-generated videos. However, the sparsity of emotional expressions in the videos poses an obstacle to visual emotion analysis. In this paper, we propose a new neural approach, Bi-stream Emotion Attribution-Classification Network (BEAC-Net), to solve three related emotion …
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computa…
Study shows stress affects emotion recognition models, improving generalizability.
problem Stress affects emotion recognition models, reducing their generalizability.
method Used adversarial networks to control for stress effects on emotion recognition.
result Emotion recognition models that control for stress during training have better generalizability.
Paper analyzes systematic jump risk around the clock using news narratives.
problem Identifying and managing priced risks in real-time market conditions.
method Combining high-frequency market data with news narratives classified by an LLM.
result Significant heterogeneity in risk premia, with macroeconomic news commanding the largest premium.
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
problem Automated emotion recognition from gait patterns.
method Data collection, preprocessing, and classification techniques.
result Gait patterns can indicate different emotion states, making them a promising source for emotion detection.
Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.
problem Understanding and recognizing emotions for human-computer interaction, safety, and medical research.
method Training CNN + RNN models with emotion category and valence/arousal representations, comparing three model types.
result Categorical emotion recognition performance improves with combined model training.
CrystalCandle creates user-friendly explanations for machine learning models.
problem Low trust in predictive models due to lack of interpretability.
method End-to-end pipeline for model interpretation, including Model Importer, Interpreter, Narrative Generator, and Exporter.
result CrystalCandle leads to higher adoption rates and improved downstream metrics.
Study finds whitepaper narratives do not predict market factor structure.
problem Predicting market behavior from cryptocurrency whitepaper claims.
method Zero-shot NLP classification combined with CP tensor decomposition of market data.
result Weak alignment between whitepaper claims and market statistics and latent factors.
Detects systematic anomalies in consumer complaints using NLP.
problem Detecting small, frequent anomalies in consumer complaints.
method NLP conversion of narratives, followed by anomaly detection algorithm.
result Demonstrates effectiveness of NLP for detecting systematic anomalies.
Achieving advancements in automatic recognition of emotions that music can induce require considering multiplicity and simultaneity of emotions. Comparison of different machine learning algorithms performing multilabel and multiclass classification is the core of our work. The study analyzes the implementation of the G…
Study shows emotion affects speaker recognition and vice versa.
problem Dependencies between emotion and speaker recognition.
method Transfer learning and fine-tuning for emotion classification.
result Fine-tuning improves emotion recognition performance by 30.40% on IEMOCAP, 7.99% on MSP-Podcast, and 8.61% on Crema-D.
Deep architecture learns transferable features for robust speech emotion recognition.
problem Robust and discriminative features for diverse speech emotion domains.
method Jointly uses CNN for domain-shared features and LSTM for domain-specific emotion classification.
result Transferable features provide gains up to 18.4% in speech emotion recognition.
EmTract extracts emotions from financial social media text.
problem Understanding investor emotions in financial markets.
method Annotated data, DistilBERT model, embedding space augmentation.
result EmTract outperforms existing emotion classifiers.
This paper presents our approach to the One-Minute Gradual-Emotion Recognition (OMG-Emotion) Challenge, focusing on dimensional emotion recognition through visual analysis of the provided emotion videos. The approach is based on a Convolutional and Recurrent (CNN-RNN) deep neural architecture we have developed for the …
This research improves emotion detection from speech, enhancing CCC by 30%.
problem Improving emotion detection from speech for categorical emotions.
method Used LSTM and TC-LSTM networks, trained with multiple datasets and robust features.
result Improved CCC for valence by 30% compared to baseline.
Emotions are intimately tied to motivation and the adaptation of behavior, and many animal species show evidence of emotions in their behavior. Therefore, emotions must be related to powerful mechanisms that aid survival, and, emotions must be evolutionary continuous phenomena. How and why did emotions evolve in nature…
End-to-end deep learning detects emotions in real-life emergency calls.
problem Recognizing emotions in real-life emergency call center recordings.
method Used an end-to-end deep learning architecture trained on IEMOCAP and CEMO datasets.
result Obtained 45.6% Unweighted Accuracy Recall on CEMO with 4 classes, 76.9% on 2 classes (Anger, Neutral).
We propose a nonparallel data-driven emotional speech conversion method. It enables the transfer of emotion-related characteristics of a speech signal while preserving the speaker's identity and linguistic content. Most existing approaches require parallel data and time alignment, which is not available in most real ap…
New method transfers emotions in facial images.
problem Transforming facial images to different emotions.
method Infinite task learning and vector-valued reproducing kernel Hilbert spaces.
result Achieves low reconstruction cost and high emotion classification accuracy.
StockEmotions dataset for financial sentiment and emotion analysis.
problem Limited resources for financial sentiment analysis.
method Collects 10,000 English comments from StockTwits, categorizes emotions into 12 classes.
result DistilBERT outperforms other models in sentiment classification, and Temporal Attention LSTM model achieves best performance in multivariate time series forecasting.
Investor emotions predict earnings announcements, but excitement lowers returns.
problem The impact of investor emotions on earnings announcements and their returns.
method Social media data analysis over a decade to test the relationship between investor emotions and earnings announcements.
result Excitement about earnings announcements is associated with lower announcement returns.
Paper uses Apprenticeship Learning to model player behavior in interactive narratives.
problem Understanding and simulating player behavior in interactive narratives.
method Receding Horizon IRL (RHIRL) to learn reward functions and policies.
result RHIRL can learn action sequences and generate behavior similar to specific players.
StarGAN model generates and recognizes emotions from facial expressions.
problem Emotion recognition and generation from facial expressions.
method Used StarGAN model to train on a new emotion dataset of 4K videos.
result Trained StarGAN model can generate and recognize emotions based on valence arousal scores.
Paper proposes a framework to protect user anonymity in emotion recognition.
problem Preserving user anonymity in face-based emotion recognition systems.
method Adversarial learning framework using CNN architecture.
result The proposed approach minimizes identity-specific information and maximizes emotion-dependent information.
Study detects SLI in children from spontaneous narrative transcripts.
problem Detecting Specific Language Impairment (SLI) in children.
method Three-stage pipeline: feature extraction, dimensionality reduction, and classification.
result 97.13% accuracy in identifying SLI from transcripts.
Generative model evaluates text emotion intensity, outperforming classification.
problem Limitations of discrete emotion classification in applied domains.
method Fine-tuning generative language models to output continuous emotion intensity scores.
result Generative model outperforms classification baselines and reveals generalization capabilities.
Bayesian model predicts emotion from fitness tracker heartbeat data.
problem Predicting emotional valence from consumer fitness tracker heartbeat data.
method End-to-end Bayesian deep learning model using PPG data.
result Peak F1 score of 0.7 for emotional valence classification.
A key aspect of word of mouth marketing are emotions. Emotions in texts help propagating messages in conventional advertising. In word of mouth scenarios, emotions help to engage consumers and incite to propagate the message further. While the function of emotions in offline marketing in general and word of mouth marke…
End-to-end model detects emotions and predicts Facebook reactions.
problem Detecting emotions and predicting reactions on Facebook posts.
method Jointly learns emotions and reactions using neural model with logic formulas as constraints.
result Model improves both emotion classification and reaction prediction.
Study on mother-infant affect communication using audio recordings.
problem Lack of accurate emotional speech databases for real-life settings.
method Used RAVDESS database and trained a Convolutional Neural Nets model.
result Dominant emotions in mother-infant speech were angry and sad.
Improved speech emotion recognition using pre-trained language models.
problem Challenging task of speech emotion recognition for natural human-machine interaction.
method Fine-tuning pre-trained language models for text emotion recognition, combining with speech emotion recognition.
result 73.5% accuracy in speech emotion recognition on a subset of IEMOCAP dataset.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
problem Understanding brain emotion circuit development during adolescence.
method Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM) for multiple DAGs estimation.
result BiLiNGAM reveals unique developmental hub structures and group-specific patterns in emotion-related intra- and inter-modular connectivity.
Narrative disclosures in 10-K filings improve bankruptcy prediction beyond accounting ratios.
problem Traditional bankruptcy prediction models rely on accounting ratios, which may not capture early warning signals.
method Developed a PB Stress Score based on distress-specific language in 10-K narratives, evaluated against accounting and dictionary benchmarks.
result Adding the PB Stress Score increases AUC from 0.8323 to 0.9019 and improves top-decile bankruptcy capture from 44.12% to 64.71%.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
problem Detecting depression and cross-cultural emotions from audiovisual data.
method Comparison of machine learning methods under standardized conditions.
result Baseline system performance on state-of-mind, depression, and cross-cultural tasks.
In emotion recognition, it is difficult to recognize human's emotional states using just a single modality. Besides, the annotation of physiological emotional data is particularly expensive. These two aspects make the building of effective emotion recognition model challenging. In this paper, we first build a multi-vie…
Automatically assessing emotional valence in human speech has historically been a difficult task for machine learning algorithms. The subtle changes in the voice of the speaker that are indicative of positive or negative emotional states are often "overshadowed" by voice characteristics relating to emotional intensity …
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
problem Improving mental health conditions through social support analysis.
method Text embedding techniques and RNNs for predicting emotional tone shifts.
result Users' emotional states can improve due to social support, as evidenced by positive comments following negative posts.
Study finds social media investor emotions predict stock prices.
problem Validation of social media sentiment models in predicting market behavior.
method Employed EmTract, an emotion model, to test social media sentiment against lab experiments.
result Firm-specific investor emotions forecast daily asset price movements.
Analyst reports contain valuable information for investment decisions.
problem Investment value in analyst reports is not fully understood or utilized.
method Embedded analyst reports with LLMs and ML forecasts of future returns.
result Portfolios formed on analyst report narratives outperform numerical forecasts and established factors.
Model improves emotion recognition using multiple physiological signals.
problem Single physiological signal is insufficient for accurate emotion recognition.
method Fused multiple modal physiological signals (EEG, EMG, EOG) for emotion classification.
result Best classification accuracy of 94.42% on arousal and 94.02% on valence in two-class tasks.