Title: Lexicon-based sentiment analysis approach for ranking event entities

Authors: Sajida Chamass; Hussein Hazimeh; Jawad Makki; Elena Mugellini; Omar Abou Khaled

Addresses: Faculty of Sciences, Lebanese University, Beirut, 6573, Lebanon ' HumanTech Institute, University of Applied Sciences and Arts of Western Switzerland, Fribourg, 1701, Switzerland ' Faculty of Sciences, Lebanese University, Beirut, 6573, Lebanon ' HumanTech Institute, University of Applied Sciences and Arts of Western Switzerland, Fribourg, 1701, Switzerland ' HumanTech Institute, University of Applied Sciences and Arts of Western Switzerland, Fribourg, 1701, Switzerland

Abstract: Social media platforms (SMP) are new resource for data analytics. Multiple aspects can be studied by using its variety of features. Sentiment analysis (SA) is a rising research topic in SMPs. SA approaches on studying and analysing events are still missing several shortcomings. In this paper, we address the problem of ranking event entities and propose a novel approach for this goal. An entity is a person who presents some task in such event, for e.g., a researcher in a conference. To achieve our target, we employ the lexical approach, in addition to associating features from both Facebook and Twitter platforms. We used Facebook reactions also, that not been used in the state-of-the-art approaches. Our results have shown that by associating both features from Facebook and Twitter and by using reactions, we can successfully rank entities participating in a specific event having high precision.

Keywords: KA; knowledge acquisition; information extraction; SNA; social network analysis; SMA; social media analytics; sentiment analysis; lexical approach.

DOI: 10.1504/IJSS.2018.091843

International Journal of Services and Standards, 2018 Vol.12 No.2, pp.126 - 139

Received: 31 May 2017
Accepted: 05 Jan 2018
Published online: 18 May 2018 *

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