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Authors: Aleksander Waloszek and Wojciech Waloszek

Affiliation: Gdansk University of Technology, Poland

Keyword(s): Sentiment Analysis, Opinion Mining, Knowledge Management, Ontologies.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Knowledge Engineering and Ontology Development ; Knowledge Representation and Reasoning ; Knowledge-Based Systems ; Natural Language Processing ; Pattern Recognition ; Symbolic Systems

Abstract: In this paper we introduce SACAM — a model for describing and classifying sentiment analysis (SA) methods. The model focuses on the knowledge used during processing textual opinions. SACAM was designed to create informative descriptions of SA methods (or classes of SA methods) and is strongly integrated with its accompanying graphical notation suited for presenting the descriptions in diagrammatical form. The paper discusses applications of SACAM and shows directions of its further development.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Waloszek, A. and Waloszek, W. (2017). SACAM - A Model for Describing and Classifying Sentiment Analysis Methods. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-220-2; ISSN 2184-433X, SciTePress, pages 196-206. DOI: 10.5220/0006199901960206

@conference{icaart17,
author={Aleksander Waloszek. and Wojciech Waloszek.},
title={SACAM - A Model for Describing and Classifying Sentiment Analysis Methods},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2017},
pages={196-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006199901960206},
isbn={978-989-758-220-2},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - SACAM - A Model for Describing and Classifying Sentiment Analysis Methods
SN - 978-989-758-220-2
IS - 2184-433X
AU - Waloszek, A.
AU - Waloszek, W.
PY - 2017
SP - 196
EP - 206
DO - 10.5220/0006199901960206
PB - SciTePress