Beyond Sentiment: The Manifold of Human Emotions

Sentiment analysis predicts the presence of positive or negative emotions in a text document. In this paper we consider higher dimensional extensions of the sentiment concept, which represent a richer set of human emotions. Our approach goes beyond previous work in that our model contains a continuous manifold rather than a finite set of human emotions. We investigate the resulting model, compare it to psychological observations, and explore its predictive capabilities. Besides obtaining significant improvements over a baseline without manifold, we are also able to visualize different notions of positive sentiment in different domains.Comment: 15 pages, 7 figure

Unveiling Emotions in Graeco-Egyptian Magical Papyri

The present study introduces a fascinating new theme in magic studies: it explores how humans experienced and expressed emotions while performing magical acts, focusing on their manifestations in various Graeco-Egyptian magical recipes and activated texts, which - although heavily formulaic in structure and in form - offer many opportunities for exploring the manifold influence of emotions on human behaviour

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Explanation of Qualia and Self-Awareness Using Elastic Membrane Concept

In this work we show that our self-awareness and perception may be successfully explained using two dimensional holistic structures with closed topology embedded into our brains - elastic membranes. These membranes are able to preserve their structure during conscious processes. Their elastic oscillations may be associated with our perceptions, where the frequency of the oscillations is responsible for the perception of different colors, sounds and other stimuli, while the amplitude of the oscillations is responsible for the feeling of a distance. According to the model the squeezed regions of a membrane correspond to the brain zones involved into awareness and attention. The model may be useful for prediction, explanation and interpretation of various conscious phenomena

Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application

Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition. The abrupt but significant dynamics for the recognition task are temporally sparse while the rest, irrelevant dynamics, are temporally redundant. In this work, we analyze and enforce sparsity constrains to learn significant temporal and spectral structures while eliminate irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneous subtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition with several sparsity levels on CASME II and SMIC, the only two publicly available spontaneous subtle emotion databases. The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016

Intelligences about things and intelligences about people

Human intelligence is redefined in light of new evidence that, in addition to general intelligence, broad mental abilities exist such as quantitative, spatial, and verbal-comprehension intelligences. Many of these broad intelligences pertain to circumscribed topics; that is, to reasoning within a broad content-area. For example, quantitative intelligence is concerned with mathematical reasoning, and spatial intelligence with reasoning about objects and their shapes and movements. Some among the broad intelligences are focused on reasoning about people: People-focused intelligences include personal intelligence (an intelligence about personality), social intelligence, and emotional intelligence. I argue for an understanding of each broad intelligence as involving a group of abilities necessary to reason about a specific subject area. To help organize the broad intelligences, a rationale is provided for categorizing them according to whether they focus mostly on things or on people