“…Kalungkutan is characterized by a drop-in voice pitch and volume and facial muscles relaxed. When Filipinos are asked to express a neutral emotion, they tend to sound sad and look sad (Cu et al, 2013 ). It varies according to durations and degrees depending on the level of attachment to the person who has gone or died.…”
This article explores the concept of suffering as experienced by Filipinos during the COVID-19 pandemic. It draws inspiration from their narratives about how they faced, managed, and struggled during this tragic event. Their experiences were interpreted and analyzed concerning Filipino culture and tradition using a modified form of thematic analysis. Findings revealed three contextualized themes: sákit (pain), pighati (grief), and pag-asa (hope). These themes are then discussed in the light of John Paul II's Salvifici Doloris. A meaningful pastoral reflection on the basic realities of suffering is formulated to clarify our understanding of such a crisis.
“…Kalungkutan is characterized by a drop-in voice pitch and volume and facial muscles relaxed. When Filipinos are asked to express a neutral emotion, they tend to sound sad and look sad (Cu et al, 2013 ). It varies according to durations and degrees depending on the level of attachment to the person who has gone or died.…”
This article explores the concept of suffering as experienced by Filipinos during the COVID-19 pandemic. It draws inspiration from their narratives about how they faced, managed, and struggled during this tragic event. Their experiences were interpreted and analyzed concerning Filipino culture and tradition using a modified form of thematic analysis. Findings revealed three contextualized themes: sákit (pain), pighati (grief), and pag-asa (hope). These themes are then discussed in the light of John Paul II's Salvifici Doloris. A meaningful pastoral reflection on the basic realities of suffering is formulated to clarify our understanding of such a crisis.
“…However, the latter is typically not varied within databases. For example, corpora contain recordings of social interactions between only Chinese [16] or only Philippine [17] nationals. While many datasets provide some information about senders' occupation, actual descriptions only span actors and students.…”
Section: A Perceivable Encoding Contextmentioning
confidence: 99%
“…An example for this are conversation partners (e.g. [15], [17], [20]): less than half of the publications that explicitly mentioned the presence of a conversation partner in the captured social interactions also used an annotation procedure where perceivers are provided with audiovisual material about these people.…”
An important aspect of human emotion perception is the use of contextual information to understand others' feelings even in situations where their behavior is not very expressive or has an emotionally ambiguous meaning. For technology to successfully detect affect, it must mimic this human ability when analyzing audiovisual input. Databases upon which machine learning algorithms are trained should capture the context of social interactions as well as the behavior expressed in them. However, there is a lack of consensus about what constitutes relevant context in such databases. In this article, we make two contributions towards overcoming this challenge: (a) we identify two principal sources of context for emotion perceptions based on psychological theory, and (b) we provide an overview of how each of these has been considered in published databases covering social interactions. Our results show that a similar set of contextual features are present across the reviewed databases. Between all the different databases researchers seem to have taken into account a set of contextual features reflecting the sources of context seen in psychological theory. However, within individual databases, these features are not yet systematically varied. This is problematic because it prevents them from being used directly as resources for the modeling of context-sensitive affect detection. Based on our findings, we suggest improvements for the future development of affective databases.
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