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Authors: Benaissa Azzeddine Rachid ; Harbaoui Azza and Ben Ghezala Henda

Affiliation: University of Manouba, RIADI Laboratory, ENSI School, La Manouba and Tunisia

Keyword(s): Opinion Mining, Sentiment Analysis, Machine Learning, Lexicon based, Ontology based, Granularity Level.

Abstract: The evolution of web 2.0 has enabled the emergence of social media where users can post, share and discuss their opinions about products, events, peoples and organizations. This increase of the user generated content (UGC) has allowed the publication of several works during the last decade in the scientific community working on sentiment analysis. Sentiment analysis, also known as opinion mining is the field of extraction and analysis of opinions, feelings and attitudes of users on the web. In this paper, we provide an overview of the field of sentiment analysis by discussing the workflow of mining opinions in different granularity levels and covering common and recent approaches and techniques used to solve tasks related to sentiment analysis process at every level.

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Paper citation in several formats:
Rachid, B.; Azza, H. and Henda, B. (2018). Sentiment Analysis Approaches based on Granularity Levels. In Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-324-7; ISSN 2184-3252, SciTePress, pages 324-331. DOI: 10.5220/0007187603240331

@conference{webist18,
author={Benaissa Azzeddine Rachid. and Harbaoui Azza. and Ben Ghezala Henda.},
title={Sentiment Analysis Approaches based on Granularity Levels},
booktitle={Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST},
year={2018},
pages={324-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007187603240331},
isbn={978-989-758-324-7},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Web Information Systems and Technologies - WEBIST
TI - Sentiment Analysis Approaches based on Granularity Levels
SN - 978-989-758-324-7
IS - 2184-3252
AU - Rachid, B.
AU - Azza, H.
AU - Henda, B.
PY - 2018
SP - 324
EP - 331
DO - 10.5220/0007187603240331
PB - SciTePress

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