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describes a novel method of network text analysis, one that involves a new
approach to 1) the selection of words from a
text, 2) the aggregation of those words into higher-order concepts, 3)
the kind of the relationship that establishes statements from pairs of concepts
and 4) the extraction of meaning from the text network formed by these statements.
After describing the method, I apply it to a sample of the seven most recent
winners of the Academy Award for Best Original Screenplay―Little Miss Sunshine, Juno, Milk, The Hurt Locker, The King’s Speech, Midnight in Paris, and Django Unchained. Consistent with
prior research, I demonstrate that structure encodes meaning. Specifically, it
is shown that statements associated with a text network’s least constrained
nodes are consistent with themes in the films’ synopses found on Wikipedia, the
International Movie Database, and Rotten Tomatoes.
In text analysis studies coders have to make qualitative decisions. These decisions are based on interpretations of the texts under study. In such situations it is very helpful to have coding rules. These do not only help as an aid to the coder, but are also useful for readers of the research report that will follow. The rules make visible in considerable extent how the coding task has been performed, they take care of transparency. This contribution focuses on motions that have been treated in the Dutch House of Representatives. Motions usually contain information on why they are needed, the proposing member usually also tells about it. There is a discussion with the secretary, who is supposed to put the motion into effect if it is accepted. The secretary even has to give an advice. It is assumed that under these discussion(s) a cognitive map containing some game theoretic representation can be found. Rules are discussed that are used to code the types of maps that might be found.
introduces a mixed music analysis method using extended specmurt analysis.
Conventional specmurt can only analyze a multi-pitch music signal from a single
instrument and cannot analyze a mixed music signal that has several different types of instruments being played at the
same time. To analyze a mixed music signal, extended specmurt is proposed. We regard the observed spectrum
extracted from the mixed music as the summation of the observed spectra corresponding to each instrument. The mixed music has as many unknown fundamental
frequency distributions as the number of instruments since the observed
spectrum of a single instrument can be expressed as a convolution of the common
harmonic structure and the fundamental frequency distribution. The relation among the observed spectrum, the common
harmonic structure and the fundamental frequency distribution is transformed
into a matrix representation in order to obtain the unknown fundamental
frequency distributions. The equation is called extended specmurt, and the matrix of unknown components can be
obtained by using a pseudo inverse matrix. The experimental result shows the
effectiveness of the proposed method.