V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12

Authors: Aitor García Pablos Montserrat Cuadros Oller German Rigaur Claramunt

Date: 01.06.2015


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Abstract

This paper presents our participation in SemEval 2015 task 12 (Aspect Based Sentiment Analysis). We participated employing only unsupervised or weakly-supervised approaches. Our attempt is based on requiring the minimum annotated or hand-crafted content, and avoids training a model using the provided training set. We use a continuous word representations (Word2Vec) to leverage in-domain semantic similarities of words for many of the involved subtasks.

BIB_text

@Article {
title = {V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12},
keywds = {

Sentiment Analysis, Opinion Mining, Unsupervised


}
abstract = {

This paper presents our participation in SemEval 2015 task 12 (Aspect Based Sentiment Analysis). We participated employing only unsupervised or weakly-supervised approaches. Our attempt is based on requiring the minimum annotated or hand-crafted content, and avoids training a model using the provided training set. We use a continuous word representations (Word2Vec) to leverage in-domain semantic similarities of words for many of the involved subtasks.


}
isbn = {978-1-941643-40-2},
date = {2015-06-01},
year = {2015},
}
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