@inproceedings{mukherjee-joshi-2014-author,
title = "Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews",
author = "Mukherjee, Subhabrata and
Joshi, Sachindra",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/467_Paper.pdf",
pages = "3092--3099",
abstract = "In this work, we propose an author-specific sentiment aggregation model for polarity prediction of reviews using an ontology. We propose an approach to construct a Phrase Annotated Author Specific Sentiment Ontology Tree (PASOT), where the facet nodes are annotated with opinion phrases of the author, used to describe the facets, as well as the author{'}s preference for the facets. We show that an author-specific aggregation of sentiment over an ontology fares better than a flat classification model, which does not take the domain-specific facet importance or author-specific facet preference into account. We compare our approach to supervised classification using Support Vector Machines, as well as other baselines from previous works, where we achieve an accuracy improvement of 7.55{\%} over the SVM baseline. Furthermore, we also show the effectiveness of our approach in capturing thwarting in reviews, achieving an accuracy improvement of 11.53{\%} over the SVM baseline.",
}
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%0 Conference Proceedings
%T Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews
%A Mukherjee, Subhabrata
%A Joshi, Sachindra
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F mukherjee-joshi-2014-author
%X In this work, we propose an author-specific sentiment aggregation model for polarity prediction of reviews using an ontology. We propose an approach to construct a Phrase Annotated Author Specific Sentiment Ontology Tree (PASOT), where the facet nodes are annotated with opinion phrases of the author, used to describe the facets, as well as the author’s preference for the facets. We show that an author-specific aggregation of sentiment over an ontology fares better than a flat classification model, which does not take the domain-specific facet importance or author-specific facet preference into account. We compare our approach to supervised classification using Support Vector Machines, as well as other baselines from previous works, where we achieve an accuracy improvement of 7.55% over the SVM baseline. Furthermore, we also show the effectiveness of our approach in capturing thwarting in reviews, achieving an accuracy improvement of 11.53% over the SVM baseline.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/467_Paper.pdf
%P 3092-3099
Markdown (Informal)
[Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews](http://www.lrec-conf.org/proceedings/lrec2014/pdf/467_Paper.pdf) (Mukherjee & Joshi, LREC 2014)
ACL