Reference : Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model

	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/10993/38467

Title :	Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model
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) >]
	Xu, Feiyu [Lenovo > AI Lab, Beijing, China]
	Schommer, Christoph [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Publication date :	2019
Main document title :	Agents and Artificial Intelligence
Publisher :	Springer
Collection and collection volume :	Lecture Notes in Computer Science, vol 11352.
Peer reviewed :	Yes
Audience :	International
ISBN :	978-3-030-05453-3
Event name :	International Conference on Agents and Artificial Intelligence 2018
Event date :	January 2018
Keywords :	[en] Sentiment Analysis ; Hawkes Process ; Personalized Model ; Attention Network ; Recurrent Neural Networks
Abstract :	[en] People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person’s past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research.
Permalink :	http://hdl.handle.net/10993/38467
DOI :	10.1007/978-3-030-05453-3_10
Other URL :	https://link.springer.com/chapter/10.1007/978-3-030-05453-3_10

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