Reference : Topic-based Historical Information Selection for Personalized Sentiment Analysis

	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/10993/39490

Title :	Topic-based Historical Information Selection for Personalized Sentiment Analysis
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 :	Apr-2019
Main document title :	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019
Peer reviewed :	Yes
Event name :	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event date :	from 24-04-2019 to 26-04-2019
Event place (city) :	Bruges
Event country :	Belgium
Keywords :	[en] Sentiment Analysis ; Information Selection ; Personalized Modelling
Abstract :	[en] In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to modeling individuality and understanding the current state of the person. We consider a user's expressions in the past as historical information, and target posts from social platforms for which Twitter texts are chosen as exemplary. While implementing the personalized model PERSEUS, we observed information loss due to the lack of flexibility regarding the design of the input sequence. To compensate this issue, we provide a procedure for information selection based on the similarities in the topics of a user's historical posts. Evaluation is conducted comparing different similarity measures, and improvements are seen with the proposed method.
Permalink :	http://hdl.handle.net/10993/39490

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