2,865 research outputs found
Learning Affect with Distributional Semantic Models
The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data
Crowdsourced intuitive visual design feedback
For many people images are a medium preferable to text and yet, with the exception of
star ratings, most formats for conventional computer mediated feedback focus on text.
This thesis develops a new method of crowd feedback for designers based on images.
Visual summaries are generated from a crowd’s feedback images chosen in response to
a design. The summaries provide the designer with impressionistic and inspiring visual
feedback. The thesis sets out the motivation for this new method, describes the
development of perceptually organised image sets and a summarisation algorithm to
implement it. Evaluation studies are reported which, through a mixed methods
approach, provide evidence of the validity and potential of the new image-based
feedback method.
It is concluded that the visual feedback method would be more appealing than text for
that section of the population who may be of a visual cognitive style. Indeed the
evaluation studies are evidence that such users believe images are as good as text when
communicating their emotional reaction about a design. Designer participants reported
being inspired by the visual feedback where, comparably, they were not inspired by
text. They also reported that the feedback can represent the perceived mood in their
designs, and that they would be enthusiastic users of a service offering this new form of
visual design feedback
Learning Affect with Distributional Semantic Models
The affective content of a text depends on the valence and emotion values of its words. At the same time a word distributional properties deeply influence its affective content. For instance a word may become negatively loaded because it tends to co-occur with other negative expressions. Lexical affective values are used as features in sentiment analysis systems and are typically estimated with hand-made resources (e.g. WordNet Affect), which have a limited coverage. In this paper we show how distributional semantic models can effectively be used to bootstrap emotive embeddings for Italian words and then compute affective scores with respect to eight basic emotions. We also show how these emotive scores can be used to learn the positive vs. negative valence of words and model behavioral data
EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop
Editor of the proceedings of EVALITA 2016
Calm-mo: An integrative tool for psychological mindfulness
Henriques has developed a “Unified Theory” that consists of eight key ideas he argues can effectively frame both the science of psychology and the practice of psychotherapy. CALM-MO, the eighth of these ideas, offers an integrative, principled approach to psychological mindfulness. CALM-MO is an acronym that encapsulates the process of cultivating a “calm” meta-cognitive observer that embodies the attitudes of curiosity, acceptance, loving compassion, and motivation toward valued states of being. Henriques posits that the idea consolidates key elements from across the various schools of thought to bring together essential therapeutic principles geared toward seeking and maintaining well-being. As such, it potentially affords the field a frame that could be readily adopted and metabolized by clinicians training in any theoretical orientation. The current project seeks to further elucidate the nature of each element of CALM-MO and to bring these threads together to develop a more nuanced understanding of each element and of how they interact. Through this process, the project aims to make the argument that CALM-MO represents a powerful integrative psychotherapeutic tool that is valuable not only for client well-being, but also for the training of beginning clinicians
Novel Methods Using Human Emotion and Visual Features for Recommending Movies
Postponed access: the file will be accessible after 2022-06-01This master thesis investigates novel methods using human emotion as contextual information to estimate and elicit ratings when watching movie trailers. The aim is to acquire user preferences without the intrusive and time-consuming behavior of Explicit Feedback strategies, and generate quality recommendations. The proposed preference-elicitation technique is implemented as an Emotion-based Filtering technique (EF) to generate recommendations, and is evaluated against two other recommendation techniques. One Visual-based Filtering technique, using low-level visual features of movies, and one Collaborative Filtering (CF) using explicit ratings. In terms of \textit{Accuracy}, we found the Emotion-based Filtering technique (EF) to perform better than the two other filtering techniques. In terms of \textit{Diversity}, the Visual-based Filtering (VF) performed best. We further analyse the obtained data to see if movie genres tend to induce specific emotions, and the potential correlation between emotional responses of users and visual features of movie trailers. When investigating emotional responses, we found that \textit{joy} and \textit{disgust} tend to be more prominent in movie genres than other emotions. Our findings also suggest potential correlations on a per movie level. The proposed Visual-based Filtering technique can be adopted as an Implicit Feedback strategy to obtain user preferences. For future work, we will extend the experiment with more participants and build stronger affective profiles to be studied when recommending movies.Masteroppgave i informasjonsvitenskapINFO390MASV-INF
Retrieval and Annotation of Music Using Latent Semantic Models
PhDThis thesis investigates the use of latent semantic models for annotation and
retrieval from collections of musical audio tracks. In particular latent semantic
analysis (LSA) and aspect models (or probabilistic latent semantic analysis,
pLSA) are used to index words in descriptions of music drawn from hundreds
of thousands of social tags. A new discrete audio feature representation is introduced
to encode musical characteristics of automatically-identified regions
of interest within each track, using a vocabulary of audio muswords. Finally a
joint aspect model is developed that can learn from both tagged and untagged
tracks by indexing both conventional words and muswords. This model is
used as the basis of a music search system that supports query by example and
by keyword, and of a simple probabilistic machine annotation system. The
models are evaluated by their performance in a variety of realistic retrieval
and annotation tasks, motivated by applications including playlist generation,
internet radio streaming, music recommendation and catalogue searchEngineering and Physical Sciences
Research Counci
Online avatar based interactions
The gridWorld project attempts to utilize 3D to develop an online multi-user visual chat system. GridWorld address ideas of how conversations in a virtual environment can be facilitated and enhanced by an abstract visual interface design. The visual interface was developed from research and examination of existing ideas, methodologies and application for development of user-embodiment, chat/virtual space, and interface useability towards the visualization of communication
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