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
Lin Gong, Mohammad Al Boni, and Hongning Wang. Modeling social norms evolution for personalized sentiment classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, volume 1, pages 855–865, 2016.
Zhen Wu, Xin-Yu Dai, Cunyan Yin, Shujian Huang, and Jiajun Chen. Improving review representations with user attention and product attention for sentiment classification. arXiv preprint arXiv:1801.07861, 2018.
Siwen Guo, Sviatlana Höhn, Feiyu Xu, and Christoph Schommer. Personalized sentiment analysis and a framework with attention-based Hawkes process model. In International Conference on Agents and Artificial Intelligence, pages 202–222. Springer, 2018.
Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.
David M Blei and John D Lafferty. Topic models. In Text Mining, pages 101–124. Chapman and Hall/CRC, 2009.
Ofir Pele and Michael Werman. Fast and robust earth mover’s distances. In 2009 IEEE 12th International Conference on Computer Vision, pages 460–467. IEEE, September 2009.
Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. From word embeddings to document distances. In International Conference on Machine Learning, pages 957–966, 2015.
Jeffrey Pennington, Richard Socher, and Christopher Manning. GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532–1543, 2014.