Sentiment Polarity Extension for Context-Sensitive Recommender Systems

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

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Paper

Sentiment Polarity Extension for Context-Sensitive Recommender Systems

Topics: Clustering and Classification Methods; Information Extraction; Machine Learning; Mining Text and Semi-Structured Data

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 0IC3K, 126-137, 2014 , Rome, Italy

Authors: Octavian Lucian Hasna ; Florin Cristian Macicasan ; Mihaela Dinsoreanu and Rodica Potolea

Affiliation: Technical University of Cluj-Napoca, Romania

Keyword(s): Sentiment Detection, Meta-Features, Classification, Text Mining, Design and Implementation, Evaluation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Clustering and Classification Methods ; Computational Intelligence ; Evolutionary Computing ; Information Extraction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Soft Computing ; Symbolic Systems

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.

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Paper citation in several formats:

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 (IC3K 2014) - KDIR; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 126-137. DOI: 10.5220/0005141101260137

@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 (IC3K 2014) - KDIR},
year={2014},
pages={126-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005141101260137},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR
TI - Sentiment Polarity Extension for Context-Sensitive Recommender Systems
SN - 978-989-758-048-2
IS - 2184-3228
AU - Hasna, O.
AU - Macicasan, F.
AU - Dinsoreanu, M.
AU - Potolea, R.
PY - 2014
SP - 126
EP - 137
DO - 10.5220/0005141101260137
PB - SciTePress