Reference : PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neur...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/34177
PERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network
English
Guo, Siwen mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Höhn, Sviatlana mailto [AI Minds > Vianden, Luxembourg]
Xu, Feiyu mailto [Lenovo > Beijing, China]
Schommer, Christoph mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Jan-2018
International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018
9
Yes
International
10th International Conference on Agents and Artificial Intelligence
from 16-01-2018 to 18-01-2018
Funchal
Portugal
[en] Sentiment Analysis ; Opinion Mining ; Personalized Memories ; Neural Networks
[en] This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between individuals and certain consistency in each individual is the cornerstone of the framework. We focus on relations between documents for user-sensitive predictions. Individual’s lexical choices act as indicators for individuality, thus we use a concept-based system which utilizes neural networks to embed concepts and associated topics in text. Furthermore, a recurrent neural network is used to memorize the history of user’s opinions, to discover user-topic dependence, and to detect implicit relations between users. PERSEUS also offers a solution for data sparsity. At the first stage, we show the benefit of inquiring a user-specified system. Improvements in performance experimented on a combined Twitter dataset are shown over generalized models. PERSEUS can be used in addition to such generalized systems to enhance the understanding of user’s opinions.
http://hdl.handle.net/10993/34177

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