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Sentiment understanding has been a long-term goal of AI in the past decades.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Thumbs up?: sentiment classification using machine learning techniques
Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002 · 2002
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Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
Peter D Turney. 2002 · 2002
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Mining and summarizing customer reviews
Minqing Hu and Bing Liu. 2004 · 2004
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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Recognizing contextual polarity in phrase-level sentiment analysis
Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005 · 2005
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Sentiment classification of movie reviews using contextual valence shifters
Alistair Kennedy and Diana Inkpen. 2006 · 2006
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Contextual valence shifters
Livia Polanyi and Annie Zaenen. 2006 · 2006
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Opinion mining and sentiment analysis
Bo Pang and Lillian Lee. 2008 · 2008
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The effect of negation on sentiment analysis and retrieval effectiveness
Lifeng Jia, Clement Yu, and Weiyi Meng. 2009 · 2009
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Review sentiment scoring via a parse-and-paraphrase paradigm
Jingjing Liu and Stephanie Seneff. 2009 · 2009
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A survey on the role of negation in sentiment analysis
Michael Wiegand, Alexandra Balahur, Benjamin Roth, Dietrich Klakow, and Andrés Montoyo. 2010 · 2010
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Semi-supervised recursive autoencoders for predicting sentiment distributions
Richard Socher, Jeffrey Pennington, Eric H Huang, Andrew Y Ng, and Christopher D Manning. 2011 · 2011
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Lexicon-based methods for sentiment analysis
Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, and Manfred Stede. 2011 · 2011
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A regression approach to affective rating of chinese words from anew
Wen-Li Wei, Chung-Hsien Wu, and Jen-Chun Lin. 2011 · 2011
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How do negation and modality impact on opinions?
Farah Benamara, Baptiste Chardon, Yannick Mathieu, Vladimir Popescu, and Nicholas Asher. 2012 · 2012
Cited alongside, same era.
Representing and resolving negation for sentiment analysis
Emanuele Lapponi, Jonathon Read, and Lilja Øvrelid. 2012 · 2012
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Statistical language models based on neural networks
Tomáš Mikolov. 2012 · 2012
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Hybrid speech recognition with deep bidirectional lstm
Alex Graves, Navdeep Jaitly, and Abdel-rahman Mohamed. 2013 · 2013
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Distributional semantic models for affective text analysis
Nikolaos Malandrakis, Alexandros Potamianos, Elias Iosif, and Shrikanth Narayanan. 2013 · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Y Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Linguistic structured sparsity in text categorization
Dani Yogatama and Noah A. Smith. 2014 · 2014
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An empirical study on the effect of negation words on sentiment
Xiaodan Zhu, Hongyu Guo, Saif Mohammad, and Svetlana Kiritchenko. 2014 · 2014
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A statistical parsing framework for sentiment classification
Li Dong, Furu Wei, Shujie Liu, Ming Zhou, and Ke Xu. 2015 · 2015
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Deep unordered composition rivals syntactic methods for text classification
Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015 · 2015
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Molding cnns for text: non-linear, non-consecutive convolutions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2015 · 2015
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Learning tag embeddings and tag-specific composition functions in recursive neural network
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Cited alongside, same era.
Tree kernel-based negation and speculation scope detection with structured syntactic parse features
Bowei Zou, Guodong Zhou, and Qiaoming Zhu. 2013 · 2013
Cited alongside, same era.
Building sentiment lexicons for all major languages
Yanqing Chen and Steven Skiena. 2014 · 2014
Cited alongside, same era.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014 · 2014
Cited alongside, same era.
Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis
Li Dong, Furu Wei, Ming Zhou, and Ke Xu. 2014 · 2014
Cited alongside, same era.
A convolutional neural network for modelling sentences
Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014 · 2014
Cited alongside, same era.
Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu, and Xiaoyan Zhu. 2015 · 2015
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Adjective intensity and sentiment analysis
Raksha Sharma, Mohit Gupta, Astha Agarwal, and Pushpak Bhattacharyya. 2015 · 2015
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Corpus-based discovery of semantic intensity scales
Chaitanya Shivade, Marie-Catherine de Marneffe, Eric Folser-Lussier, and Albert Lai. 2015 · 2015
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Improved semantic representations from tree-structured long short-term memory networks
Kai Sheng Tai, Richard Socher, and Christopher D Manning. 2015 · 2015
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Long short-term memory over recursive structures
Xiaodan Zhu, Parinaz Sobhani, and Hongyu Guo. 2015 · 2015
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Neural networks for negation scope detection
Federico Fancellu, Adam Lopez, and Bonnie Webber. 2016 · 2016
Closest in time.
Context-sensitive lexicon features for neural sentiment analysis
Zhiyang Teng, Duy-Tin Vo, and Yue Zhang. 2016 · 2016
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Don’t count, predict! an automatic approach to learning sentiment lexicons for short text
Duy Tin Vo and Yue Zhang. 2016 · 2016
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Ecnu at semeval-2016 task 7: An enhanced supervised learning method for lexicon sentiment intensity ranking
Feixiang Wang, Zhihua Zhang, and Man Lan. 2016 · 2016
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