2,524 research outputs found
Affection Driven Neural Networks for Sentiment Analysis
Deep neural network models have played a critical role in sentiment analysis with promising results in the recent decade. One of the essential challenges, however, is how external sentiment knowledge can be effectively utilized. In this work, we propose a novel affection-driven approach to incorporating affective knowledge into neural network models. The affective knowledge is obtained in the form of a lexicon under the Affect Control Theory (ACT), which is represented by vectors of three-dimensional attributes in Evaluation, Potency, and Activity (EPA). The EPA vectors are mapped to an affective influence value and then integrated into Long Short-term Memory (LSTM) models to highlight affective terms. Experimental results show a consistent improvement of our approach over conventional LSTM models by 1.0% to 1.5% in accuracy on three large benchmark datasets. Evaluations across a variety of algorithms have also proven the effectiveness of leveraging affective terms for deep model enhancement
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis
Sarcasm, sentiment, and emotion are three typical kinds of spontaneous
affective responses of humans to external events and they are tightly
intertwined with each other. Such events may be expressed in multiple
modalities (e.g., linguistic, visual and acoustic), e.g., multi-modal
conversations. Joint analysis of humans' multi-modal sarcasm, sentiment, and
emotion is an important yet challenging topic, as it is a complex cognitive
process involving both cross-modality interaction and cross-affection
correlation. From the probability theory perspective, cross-affection
correlation also means that the judgments on sarcasm, sentiment, and emotion
are incompatible. However, this exposed phenomenon cannot be sufficiently
modelled by classical probability theory due to its assumption of
compatibility. Neither do the existing approaches take it into consideration.
In view of the recent success of quantum probability (QP) in modeling human
cognition, particularly contextual incompatible decision making, we take the
first step towards introducing QP into joint multi-modal sarcasm, sentiment,
and emotion analysis. Specifically, we propose a QUantum probabIlity driven
multi-modal sarcasm, sEntiment and emoTion analysis framework, termed QUIET.
Extensive experiments on two datasets and the results show that the
effectiveness and advantages of QUIET in comparison with a wide range of the
state-of-the-art baselines. We also show the great potential of QP in
multi-affect analysis
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability
Traditional sentiment construction in finance relies heavily on the
dictionary-based approach, with a few exceptions using simple machine learning
techniques such as Naive Bayes classifier. While the current literature has not
yet invoked the rapid advancement in the natural language processing, we
construct in this research a textual-based sentiment index using a novel model
BERT recently developed by Google, especially for three actively trading
individual stocks in Hong Kong market with hot discussion on Weibo.com. On the
one hand, we demonstrate a significant enhancement of applying BERT in
sentiment analysis when compared with existing models. On the other hand, by
combining with the other two existing methods commonly used on building the
sentiment index in the financial literature, i.e., option-implied and
market-implied approaches, we propose a more general and comprehensive
framework for financial sentiment analysis, and further provide convincing
outcomes for the predictability of individual stock return for the above three
stocks using LSTM (with a feature of a nonlinear mapping), in contrast to the
dominating econometric methods in sentiment influence analysis that are all of
a nature of linear regression.Comment: 10 pages, 1 figure, 5 tables, submitted to NeurIPS 2019, under revie
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Eye-Tracking Signals Based Affective Classification Employing Deep Gradient Convolutional Neural Networks
Utilizing biomedical signals as a basis to calculate the human affective states is an essential issue of affective computing (AC). With the in-depth research on affective signals, the combination of multi-model cognition and physiological indicators, the establishment of a dynamic and complete database, and the addition of high-tech innovative products become recent trends in AC. This research aims to develop a deep gradient convolutional neural network (DGCNN) for classifying affection by using an eye-tracking signals. General
signal process tools and pre-processing methods were applied firstly, such as Kalman filter, windowing with hamming, short-time Fourier transform (SIFT), and fast Fourier transform (FTT). Secondly, the eye-moving and tracking signals were converted into images. A convolutional neural networks-based training structure was subsequently applied; the experimental dataset was acquired by an eye-tracking device by assigning four affective stimuli (nervous, calm, happy, and sad) of 16 participants. Finally, the performance of DGCNN was compared with a decision tree (DT), Bayesian Gaussian model (BGM), and k-nearest neighbor (KNN) by using indices of true positive rate (TPR) and false negative rate (FPR). Customizing mini-batch, loss, learning rate, and gradients definition for the training structure of the deep neural network was also deployed finally. The predictive classification matrix showed the effectiveness of the proposed method for eye moving and tracking signals, which performs more than 87.2% inaccuracy. This research provided a feasible way to find more natural human-computer interaction through eye moving and tracking signals and has potential application on the affective production design process
Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data
Accurate forecasting and analysis of emerging pandemics play a crucial role
in effective public health management and decision-making. Traditional
approaches primarily rely on epidemiological data, overlooking other valuable
sources of information that could act as sensors or indicators of pandemic
patterns. In this paper, we propose a novel framework called MGL4MEP that
integrates temporal graph neural networks and multi-modal data for learning and
forecasting. We incorporate big data sources, including social media content,
by utilizing specific pre-trained language models and discovering the
underlying graph structure among users. This integration provides rich
indicators of pandemic dynamics through learning with temporal graph neural
networks. Extensive experiments demonstrate the effectiveness of our framework
in pandemic forecasting and analysis, outperforming baseline methods across
different areas, pandemic situations, and prediction horizons. The fusion of
temporal graph learning and multi-modal data enables a comprehensive
understanding of the pandemic landscape with less time lag, cheap cost, and
more potential information indicators
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