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Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TLDR
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. Expand
From Frequency to Meaning: Vector Space Models of Semantics
TLDR
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. Expand
Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses PointwiseExpand
Measuring praise and criticism: Inference of semantic orientation from association
TLDR
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. Expand
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
TLDR
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. Expand
Learning Algorithms for Keyphrase Extraction
TLDR
The experimental results support the claim that a custom-designed algorithm (GenEx), incorporating specialized procedural domain knowledge, can generate better keyphrases than a general-purpose algorithm (C4.5). Expand
Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm
TLDR
This paper introduces ICET, a new algorithm for cost-sensitive classification that uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm and establishes that ICET performs significantly better than its competitors. Expand
Literal and Metaphorical Sense Identification through Concrete and Abstract Context
TLDR
An algorithm is introduced that uses the hypothesis that metaphorical word usage is correlated with the degree of abstractness of the word's context to classify a word sense in a given context as either literal (denotative) or metaphorical (connotative). Expand
Similarity of Semantic Relations
TLDR
LRA extends the VSM approach in three ways: the patterns are derived automatically from the corpus, the Singular Value Decomposition (SVD) is used to smooth the frequency data, and automatically generated synonyms are used to explore variations of the word pairs. Expand
Emotions Evoked by Common Words and Phrases: Using Mechanical Turk to Create an Emotion Lexicon
TLDR
This paper shows how to create a high-quality, moderate-sized emotion lexicon using Mechanical Turk, and identifies which emotions tend to be evoked simultaneously by the same term and shows that certain emotions indeed go hand in hand. Expand
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