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 politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties' average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual's political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual's political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens' voting behavior on the other.

Shallow Domain Adaptive Embeddings for Sentiment Analysis

“…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.…”

Publish less, read more

“…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.…”

Idiosyncratic Tower of Babel: Individual differences in word meaning representation increase along abstractness

“…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 .…”

“…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] .…”