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One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on.
Occam’s razor
Anselm Blumer, Andrzej Ehrenfeucht, David Haussler, and Manfred K Warmuth. 1987 · 1987
Earlier work this paper cites.
A learning algorithm for continually running fully recurrent neural networks
Ronald J Williams and David Zipser. 1989 · 1989
Earlier work this paper cites.
Convolutional networks for images, speech, and time series
Yann LeCun, Yoshua Bengio, et al. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
John Lafferty, Andrew McCallum, and Fernando CN Pereira. 2001 · 2001
Earlier work this paper cites.
Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
Earlier work this paper cites.
Extracting product features and opinions from reviews
Ana-Maria Popescu and Oren Etzioni. 2005 · 2005
Earlier work this paper cites.
Movie review mining and summarization
Li Zhuang, Feng Jing, and Xiao-Yan Zhu. 2006 · 2006
Earlier work this paper cites.
Topic sentiment mixture: Modeling facets and opinions in weblogs
Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007 · 2007
Earlier work this paper cites.
Opinion mining and sentiment analysis
Bo Pang and Lillian Lee. 2008 · 2008
Earlier work this paper cites.
A joint model of text and aspect ratings for sentiment summarization
Ivan Titov and Ryan McDonald. 2008 · 2008
Earlier work this paper cites.
Bootstrapping both product features and opinion words from chinese customer reviews with cross-inducing
Bo Wang and Houfeng Wang. 2008 · 2008
Earlier work this paper cites.
Joint sentiment/topic model for sentiment analysis
Chenghua Lin and Yulan He. 2009 · 2009
Earlier work this paper cites.
Extracting opinion targets in a single- and cross-domain setting with conditional random fields
Niklas Jakob and Iryna Gurevych. 2010 · 2010
Earlier work this paper cites.
ILDA: interdependent lda model for learning latent aspects and their ratings from online product reviews
Samaneh Moghaddam and Martin Ester. 2011 · 2011
Earlier work this paper cites.
Opinion word expansion and target extraction through double propagation
Guang Qiu, Bing Liu, Jiajun Bu, and Chun Chen. 2011 · 2011
Earlier work this paper cites.
Sentic Computing Techniques, Tools, and Applications 2nd Edition
Erik Cambria and Amir Hussain. 2012 · 2012
Cited alongside, same era.
Sentiment Analysis and Opinion Mining
Bing Liu. 2012 · 2012
Cited alongside, same era.
Opinion target extraction using partially-supervised word alignment model
Kang Liu, Liheng Xu, Yang Liu, and Jun Zhao. 2013 · 2013
Cited alongside, same era.
Open domain targeted sentiment
Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013 · 2013
Cited alongside, same era.
Collective opinion target extraction in Chinese microblogs
Xinjie Zhou, Xiaojun Wan, and Jianguo Xiao. 2013 · 2013
Cited alongside, same era.
Ihs r&d belarus: Cross-domain extraction of product features using crf
Maryna Chernyshevich. 2014 · 2014
Cited alongside, same era.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley. 2016 · 2016
Later among the works it cites.
Semeval-2016 task 5: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, AL-Smadi Mohammad, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, et al. 2016 · 2016
Later among the works it cites.
Aspect extraction for opinion mining with a deep convolutional neural network
Soujanya Poria, Erik Cambria, and Alexander Gelbukh. 2016 · 2016
Later among the works it cites.
Nlangp at semeval-2016 task 5: Improving aspect based sentiment analysis using neural network features
Zhiqiang Toh and Jian Su. 2016 · 2016
Later among the works it cites.
Recursive neural conditional random fields for aspect-based sentiment analysis
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2016 · 2016
Later among the works it cites.
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Convolutional neural networks for sentence classification
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Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Dependency-based word embeddings
Omer Levy and Yoav Goldberg. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Semeval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
Cited alongside, same era.
Unsupervised cross-domain word representation learning
Danushka Bollegala, Takanori Maehara, and Ken-ichi Kawarabayashi. 2015 · 2015
Cited alongside, same era.
Unsupervised word and dependency path embeddings for aspect term extraction
Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, and Ming Zhou. 2016 · 2016
Later among the works it cites.
Think globally, embed locally—locally linear meta-embedding of words
Danushka Bollegala, Kohei Hayashi, and Ken-ichi Kawarabayashi. 2017 · 2017
Later among the works it cites.
Improving sentiment analysis via sentence type classification using bilstm-crf and cnn
Tao Chen, Ruifeng Xu, Yulan He, and Xuan Wang. 2017 · 2017
Later among the works it cites.
Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017 · 2017
Later among the works it cites.
An unsupervised neural attention model for aspect extraction
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2017 · 2017
Later among the works it cites.
Deep multi-task learning for aspect term extraction with memory interaction
Xin Li and Wai Lam. 2017 · 2017
Later among the works it cites.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
Nils Reimers and Iryna Gurevych. 2017 · 2017
Later among the works it cites.
Lifelong learning crf for supervised aspect extraction
Lei Shu, Hu Xu, and Bing Liu. 2017 · 2017
Later among the works it cites.
Fast and accurate entity recognition with iterated dilated convolutions
Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 2017 · 2017
Later among the works it cites.
Coupled multi-layer attentions for co-extraction of aspect and opinion terms
Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, and Xiaokui Xiao. 2017 · 2017
Later among the works it cites.