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Topic-based Historical Information Selection for Personalized Sentiment Analysis
Guo, Siwen; Höhn, Sviatlana; Schommer, Christoph
2019In European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019
Peer reviewed
 

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Keywords :
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.
Disciplines :
Computer science
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)
External co-authors :
no
Language :
English
Title :
Topic-based Historical Information Selection for Personalized Sentiment Analysis
Publication date :
April 2019
Event name :
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Event place :
Bruges, Belgium
Event date :
from 24-04-2019 to 26-04-2019
Main work title :
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 14 May 2019

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