Sentiment Polarity Extension for Context-Sensitive Recommender Systems

Octavian Lucian Hasna, Florin Cristian Macicasan, Mihaela Dinsoreanu, Rodica Potolea

2014

Abstract

Opinion mining has become an important field of text mining. The limitations in case of supervised learning refer to domain dependence: a solution is highly dependent (if not specifically designed or at least specifically tuned) on a given data set (or at least specific domain). Our method is an attempt to overcome such limitations by considering the generic characteristics hidden in textual information. We aim to identify the sentiment polarity of documents that are part of different domains with the help of a uniform, cross-domain representation. It relies on three classes of original meta-features that can be used to characterize datasets belonging to various domains. We evaluate our approach using three datasets extensively used in the literature. The results for in-domain and cross-domain verification show that the proposed approach handles novel domains increasingly better as its training corpus grows, thus inducing domain-independence.

Paper Citation

in Harvard Style

Hasna O., Macicasan F., Dinsoreanu M. and Potolea R. (2014). Sentiment Polarity Extension for Context-Sensitive Recommender Systems . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 126-137. DOI: 10.5220/0005141101260137

in Bibtex Style

@conference{kdir14,
author={Octavian Lucian Hasna and Florin Cristian Macicasan and Mihaela Dinsoreanu and Rodica Potolea},
title={Sentiment Polarity Extension for Context-Sensitive Recommender Systems },
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={126-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005141101260137},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Sentiment Polarity Extension for Context-Sensitive Recommender Systems
SN - 978-989-758-048-2
AU - Hasna O.
AU - Macicasan F.
AU - Dinsoreanu M.
AU - Potolea R.
PY - 2014
SP - 126
EP - 137
DO - 10.5220/0005141101260137