27,616 research outputs found

    Positivity of the English language

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    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

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    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

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    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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