V3: Unsupervised Aspect Based Sentiment Analysis for SemEval2015 Task 12
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
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},
}
