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Authors: Pierluca Sangiorgi 1 ; Agnese Augello 2 and Giovanni Pilato 2

Affiliations: 1 CNR, Consiglio Nazionale delle Ricerche, INAF and Istituto di Astrofisica Spaziale e Fisica Cosmica - Palermo, Italy ; 2 CNR and Consiglio Nazionale delle Ricerche, Italy

Keyword(s): Subjectivity Analysis, Sentiment Analysis, Opinion Mining, Machine Learning.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Sentiment Analysis is a discipline that aims at identifying and extract the subjectivity expressed by authors of information sources. Sentiment Analysis can be applied at different level of granularity and each of them still has open issues. In this paper we propose a completely unsupervised approach aimed at inducing a set of words patterns that change the polarity of subjective terms. This is a very important task because, while sentiment lexicons are valid tools that can be used to identify the polarity at word level, working at different level of granularity they are no longer sufficient, because of the various aspects to consider like the context, the use of negations and so on that can change the polarity of subjective terms.

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Paper citation in several formats:
Sangiorgi, P.; Augello, A. and Pilato, G. (2014). An Approach to Detect Polarity Variation Rules for Sentiment Analysis. In Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-024-6; ISSN 2184-3252, SciTePress, pages 344-349. DOI: 10.5220/0004961903440349

@conference{webist14,
author={Pierluca Sangiorgi. and Agnese Augello. and Giovanni Pilato.},
title={An Approach to Detect Polarity Variation Rules for Sentiment Analysis},
booktitle={Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2014},
pages={344-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004961903440349},
isbn={978-989-758-024-6},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - An Approach to Detect Polarity Variation Rules for Sentiment Analysis
SN - 978-989-758-024-6
IS - 2184-3252
AU - Sangiorgi, P.
AU - Augello, A.
AU - Pilato, G.
PY - 2014
SP - 344
EP - 349
DO - 10.5220/0004961903440349
PB - SciTePress

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