P
Peter D. Turney
Researcher at National Research Council
Publications - 112
Citations - 26597
Peter D. Turney is an academic researcher from National Research Council. The author has contributed to research in topics: Semantic similarity & Semantics. The author has an hindex of 52, co-authored 112 publications receiving 24976 citations. Previous affiliations of Peter D. Turney include University of Toronto & Allen Institute for Artificial Intelligence.
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Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
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From frequency to meaning: vector space models of semantics
Peter D. Turney,Patrick Pantel +1 more
TL;DR: The goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs, and to provide pointers into the literature for those who are less familiar with the field.
Proceedings ArticleDOI
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: In this article, an unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended(thumbs down) is presented. But the classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs.
Journal ArticleDOI
Crowdsourcing a word–emotion association lexicon
Saif M. Mohammad,Peter D. Turney +1 more
TL;DR: It is shown how the combined strength and wisdom of the crowds can be used to generate a large, high‐quality, word–emotion and word–polarity association lexicon quickly and inexpensively.
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Measuring praise and criticism: Inference of semantic orientation from association
TL;DR: This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words, based on two different statistical measures of word association.