We describe the systems we have used for participating in Subtasks D (binary quantification) and E (ordinal quantification) of SemEval-2016 Task 4 "Sentiment Analysis in Twitter". The binary quantification system uses a "Probabilistic Classify and Count" (PCC) approach that leverages the calibrated probabilities obtained from the output of an SVM. The ordinal quantification approach uses an ordinal tree of PCC binary quantifiers, where the tree is generated via a splitting criterion that minimizes the ordinal quantification loss.

QCRI at SemEval-2016 Task 4: Probabilistic methods for binary and ordinal quantification

Sebastiani F
2016

Abstract

We describe the systems we have used for participating in Subtasks D (binary quantification) and E (ordinal quantification) of SemEval-2016 Task 4 "Sentiment Analysis in Twitter". The binary quantification system uses a "Probabilistic Classify and Count" (PCC) approach that leverages the calibrated probabilities obtained from the output of an SVM. The ordinal quantification approach uses an ordinal tree of PCC binary quantifiers, where the tree is generated via a splitting criterion that minimizes the ordinal quantification loss.
2016
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
978-1-941643-95-2
Sentiment classification
ARTIFICIAL INTELLIGENCE. Learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/324340
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