2017
DOI: 10.3758/s13428-017-0931-5
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Speaking two “Languages” in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently

Abstract: In this article we report a computational semantic analysis of the presidential candidates' speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of… Show more

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Cited by 12 publications
(17 citation statements)
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“…Comparing the gold standard list with the list derived from the DA embedding demonstrates the efficacy of the method. This experiment adopts the list of politically contentious words that appear in (Li et al, 2017). From the tokenized tweets of the users, construct a vocabulary of words V Lib and V Con that represent the Liberal and Conservative tweets respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Comparing the gold standard list with the list derived from the DA embedding demonstrates the efficacy of the method. This experiment adopts the list of politically contentious words that appear in (Li et al, 2017). From the tokenized tweets of the users, construct a vocabulary of words V Lib and V Con that represent the Liberal and Conservative tweets respectively.…”
Section: Methodsmentioning
confidence: 99%
“…Many wonderful applications of Word2Vec, in such diverse fields as machine translation, music classification and recommendation, and automatic extraction of gene disease associations, have already been developed. The most pertinent to the present purposes may be the semantic space analysis of election debates by US presidential candidates from 1999 to 2016 (Li et al, 2017), revealing distinct semantic spaces of Democratic and Republican parties and even of individual candidates. Similarity was operationalized here by the mean Euclidean distance between key concepts in the two semantic spaces.…”
Section: Potential Remediesmentioning
confidence: 99%
“…Such commonality is the basis of effective learning and communication when speaking the same language and word meaning misalignment is usually only discussed within the context of cross-language speakers 1,2 . However, there are also intriguing individual variations in how we understand a word within a language, which associate with important nonverbal properties, such as political position 3 or emotional perception 4 . The question of individual variation in word representation has intrigued classical philosophers including John Locke and Bertrand Russell, who put forward opposite speculations: that words denoting "complex ideas" (e.g., abstract words) may have lower inter-subject consistency (ISC) 5 ; and that words entailing more "abstractness of logic" may have greater individual consistency 6 .…”
Section: Main Textmentioning
confidence: 99%
“…We constructed cognitive word meaning representations from behavioral judgments of the semantic distances among 90 words. This approach was taken because word meaning representation is difficult to capture by explicit definitions 18,19 and it is assumed to be (at least partly) represented by the relationships with other words 3,4,[20][21][22] .…”
Section: Cognitive Representations Of Word Meaning: Individual Consismentioning
confidence: 99%