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What and Who

Exploring new feature space for Sentiment Analysis

Kashyap Popat
International Max Planck Research School for Computer Science - IMPRS
IMPRS Research Seminar
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI  
Public Audience
English

Date, Time and Location

Monday, 9 November 2015
12:05
-- Not specified --
E1 4
024
Saarbrücken

Abstract

Expensive feature engineering based on word senses has been shown to be useful for document level sentiment classification.

A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be
achieved with a lesser effort through the means of syntagma based word clustering which addresses the problem of data sparsity
in sentiment analysis, both monolingual and cross-lingual.

In this talk, I will present the evolution of features in document level sentiment classification along with the brief introduction of
my current research work.

Contact

Andrea Ruffing
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Andrea Ruffing, 11/05/2015 17:36 -- Created document.