Reference : Topic-based Historical Information Selection for Personalized Sentiment Analysis
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/39490
Topic-based Historical Information Selection for Personalized Sentiment Analysis
English
Guo, Siwen mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Höhn, Sviatlana mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Schommer, Christoph mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Apr-2019
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019
Yes
27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
from 24-04-2019 to 26-04-2019
Bruges
Belgium
[en] Sentiment Analysis ; Information Selection ; Personalized Modelling
[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.
http://hdl.handle.net/10993/39490

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