A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
AVEC 2019 challenges AI in detecting depression and cross-cultural emotions.
Bayesian model predicts emotion from fitness tracker heartbeat data.
Study evaluates feature selection methods for emotion recognition in resource-constrained settings.
AI predicts dementia onset from emotional face evaluations.
The study predicts how discussions in mental disorder Reddit communities affect users' emotional states.
Emotions play a crucial role in human interaction, health care and security investigations and monitoring. Automatic emotion recognition (AER) using electroencephalogram (EEG) signals is an effective method for decoding the real emotions, which are independent of body gestures, but it is a challenging problem. Several …
StockEmotions dataset for financial sentiment and emotion analysis.
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 …
Domestic Violence (DV) is considered as big social issue and there exists a strong relationship between DV and health impacts of the public. Existing research studies have focused on social media to track and analyse real world events like emerging trends, natural disasters, user sentiment analysis, political opinions,…
Dynamic topic model improves mental health note analysis for children.
Among American women, the rate of breast cancer is only second to lung cancer. An estimated 12.4% women will develop breast cancer over the course of their lifetime. The widespread use of social media across the socio-economic spectrum offers unparalleled ways to facilitate information sharing, in particular as it pert…
Generative model evaluates text emotion intensity, outperforming classification.
We propose a novel approach to multimodal sentiment analysis using deep neural networks combining visual analysis and natural language processing. Our goal is different than the standard sentiment analysis goal of predicting whether a sentence expresses positive or negative sentiment; instead, we aim to infer the laten…
Project extends emotion recognition database and trains neural networks for categorical and dimensional emotions.
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 …
Gait patterns reveal emotions, offering a non-invasive method for automated recognition.
Investor emotions predict earnings announcements, but excitement lowers returns.
BiLiNGAM model reveals brain emotion circuit development in adolescents.
Study shows emotion affects speaker recognition and vice versa.
End-to-end model detects emotions and predicts Facebook reactions.
FINs enhance performance in diverse datasets like finance, speech, and health.
Study finds social media investor emotions predict stock prices.
Following the financial crisis of the late 2000s, policy makers have shown considerable interest in monitoring financial stability. Several central banks now publish indices of financial stress, which are essentially based upon market related data. In this paper, we examine the potential for improving the indices by de…
New method considers subjectivity in text analysis using 'Room Theory'.
More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal,…
This study applies variational inference to improve music emotion recognition.
Estimating the intensity of emotion has gained significance as modern textual inputs in potential applications like social media, e-retail markets, psychology, advertisements etc., carry a lot of emotions, feelings, expressions along with its meaning. However, the approaches of traditional sentiment analysis primarily …
Over the past few years many research efforts have been devoted to the field of affect analysis. Various approaches have been proposed for: i) discrete emotion recognition in terms of the primary facial expressions; ii) emotion analysis in terms of facial Action Units (AUs), assuming a fixed expression intensity; iii) …
Study uses NLP to analyze emotions and challenges of young people with IDD.
We design, conduct and present the results of a highly personalized baseline emotion recognition experiment, which aims to set reliable ground-truth estimates for the subject's emotional state for real-life prediction under similar conditions using a small number of physiological sensors. We also propose an adaptive st…
Unimodal approach for group-level emotion recognition without individual features.
Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition. They map the autoencoder's bottleneck layer output (termed as code vectors) to different no…
Simpler CNN model with spatial attention and temporal pooling outperforms complex models.
Generative Adversarial Networks improve affective speech feature generation.
Privacy-preserving method protects user speech data from cloud services.
System detects financial forecasts in tweets, achieving high precision.
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.
This paper fine-tunes BERT for stock market sentiment analysis and improves trading performance.
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…
NLP tasks are often limited by scarcity of manually annotated data. In social media sentiment analysis and related tasks, researchers have therefore used binarized emoticons and specific hashtags as forms of distant supervision. Our paper shows that by extending the distant supervision to a more diverse set of noisy la…
Deep architecture learns transferable features for robust speech emotion recognition.
Paper proposes ICCN to learn correlations between text, audio, and video for multimodal sentiment analysis.
EmTract extracts emotions from financial social media text.
This research improves emotion detection from speech, enhancing CCC by 30%.
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.
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…