E
Eyal Sagi
Researcher at Northwestern University
Publications - 33
Citations - 761
Eyal Sagi is an academic researcher from Northwestern University. The author has contributed to research in topics: Sentence & Latent semantic analysis. The author has an hindex of 12, co-authored 33 publications receiving 646 citations. Previous affiliations of Eyal Sagi include University of St. Francis.
Papers
More filters
Proceedings ArticleDOI
Semantic Density Analysis: Comparing Word Meaning across Time and Phonetic Space
TL;DR: A new statistical method for detecting and tracking changes in word meaning, based on Latent Semantic Analysis, which allows researchers to make statistical inferences on questions such as whether the meaning of a word changed across time or if a phonetic cluster is associated with a specific meaning.
Journal ArticleDOI
Purity homophily in social networks.
Morteza Dehghani,Kate M. Johnson,Joe Hoover,Eyal Sagi,Justin Garten,Niki Parmar,Stephen Vaisey,Rumen Iliev,Jesse Graham +8 more
TL;DR: Results indicate that social network processes reflect moral selection, and both online and offline differences in moral purity concerns are particularly predictive of social distance.
Journal ArticleDOI
Measuring Moral Rhetoric in Text
Eyal Sagi,Morteza Dehghani +1 more
TL;DR: A computational text analysis technique for measuring the moral loading of concepts as they are used in a corpus, using latent semantic analysis to compute the semantic similarity between concepts and moral keywords taken from the “Moral foundation Dictionary”.
Tracing semantic change with latent semantic analysis
TL;DR: This paper presents a method that uses Latent Semantic Analysis (Landauer, Foltz & Laham, 1998) to automatically track and identify semantic changes across a corpus and demonstrates its potential by applying it to several well-known examples of semantic change in the history of the English language.
Journal ArticleDOI
Automated text analysis in psychology: methods, applications, and future developments*
TL;DR: It is concluded that the constant increase of computational power and the wide availability of textual data will inevitably make automated text analysis a common tool for psychologists.