27,616 research outputs found
Positivity of the English language
Over the last million years, human language has emerged and evolved as a
fundamental instrument of social communication and semiotic representation.
People use language in part to convey emotional information, leading to the
central and contingent questions: (1) What is the emotional spectrum of natural
language? and (2) Are natural languages neutrally, positively, or negatively
biased? Here, we report that the human-perceived positivity of over 10,000 of
the most frequently used English words exhibits a clear positive bias. More
deeply, we characterize and quantify distributions of word positivity for four
large and distinct corpora, demonstrating that their form is broadly invariant
with respect to frequency of word use.Comment: Manuscript: 9 pages, 3 tables, 5 figures; Supplementary Information:
12 pages, 3 tables, 8 figure
Chasing Success: A Cultivated Reality
George Gerbner’s cultivation theory claims that people who consume heavy amounts of media are more likely to be influenced by those messages to believe the media reality as opposed to actual reality. Using cultivation theory as the basis for study, I performed a cultivation analysis examining the intersection of mass media and perceptions of success among college-aged young adults living in the United States. The analysis focused on three main points: (1) How mass media perceives and subsequently demonstrates success. (2) The impact of mass media on young adults living in America. (3) What reality of success is cultivated by these young adults. The top five most-watched music videos from the past five years were analyzed for perceptions of success. Seventy-nine students from Bryant University were surveyed. A message analysis of the music videos revealed that wealth as well as conformity to certain standards of physical perfection and gender-specific behaviors were key elements of success. This study found that college-aged young adults who are heavier consumers of music videos tended to share the perceptions of success as perpetuated by the media over those who are light viewers. However, there were certain elements of success where the intersection of college-aged young adults’ perceptions of success and media were more nuanced and complicated
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
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