Reference : A Personalized Sentiment Model with Textual and Contextual Information

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/40963

Title :	A Personalized Sentiment Model with Textual and Contextual Information
Language :	English
Author, co-author :	Guo, Siwen [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Höhn, Sviatlana [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
	Schommer, Christoph [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	Nov-2019
Main document title :	The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019
Peer reviewed :	Yes
Event name :	23rd SIGNLL Conference on Computational Natural Language Learning (CoNLL)
Event date :	November 3-4, 2019
Event place (city) :	Hong Kong
Keywords :	[en] Sentiment Analysis ; Personalized Modeling ; Information Merging
Abstract :	[en] In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.
Permalink :	http://hdl.handle.net/10993/40963

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