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Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review.
When more pain is preferred to less: Adding a better end
Daniel Kahneman, Barbara L Fredrickson, Charles A Schreiber, and Donald A Redelmeier. 1993 · 1993
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Integration of the cognitive and the psychodynamic unconscious
Seymour Epstein. 1994 · 1994
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Predicting the semantic orientation of adjectives
Vasileios Hatzivassiloglou and Kathleen R McKeown. 1997 · 1997
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Ego depletion: is the active self a limited resource?
RF Baumeister, E Bratslavsky, M Muraven, and DM Tice. 1998 · 1998
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The emotional brain: The mysterious underpinnings of emotional life
Joseph E LeDoux. 1998 · 1998
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Effects of adjective orientation and gradability on sentence subjectivity
Vasileios Hatzivassiloglou and Janyce M Wiebe. 2000 · 2000
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Causality: Models, reasoning and inference
Judea Pearl et al. 2000 · 2000
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Direct and indirect effects
Judea Pearl. 2001 · 2001
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Representativeness revisited: Attribute substitution in intuitive judgment , pages 49–81. Cambridge University Press
Daniel Kahneman and Shane Frederick. 2002 · 2002
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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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Sentiment analysis: Capturing favorability using natural language processing
Tetsuya Nasukawa and Jeonghee Yi. 2003 · 2003
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Emotional intelligence
Peter Salovey and John D Mayer. 2004 · 2004
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Solving the emotion paradox: Categorization and the experience of emotion
Lisa Feldman Barrett. 2006 · 2006
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On causally asymmetric versions of occam’s razor and their relation to thermodynamics
Dominik Janzing. 2007 · 2007
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Learning with compositional semantics as structural inference for subsentential sentiment analysis
Yejin Choi and Claire Cardie. 2008 · 2008
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Nonlinear causal discovery with additive noise models
Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf. 2008 · 2008
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Adapting naive bayes to domain adaptation for sentiment analysis
Songbo Tan, Xueqi Cheng, Yuefen Wang, and Hongbo Xu. 2009 · 2009
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Causal inference using the algorithmic Markov condition
Dominik Janzing and Bernhard Schölkopf. 2010 · 2010
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Identifying cause and effect on discrete data using additive noise models
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2010 · 2010
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Thinking, fast and slow
Daniel Kahneman. 2011 · 2011
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Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
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Information-geometric approach to inferring causal directions
Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, and Bernhard Schölkopf. 2012 · 2012
Cited alongside, same era.
On causal and anticausal learning
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris M. Mooij. 2012 · 2012
Cited alongside, same era.
Lesion studies of human emotion and feeling
Justin S Feinstein. 2013 · 2013
Cited alongside, same era.
The effects of measuring emotion: Physiological reactions to emotional situations depend on whether someone is asking
Karim S Kassam and Wendy Berry Mendes. 2013 · 2013
Cited alongside, same era.
Document-level sentiment classification: An empirical comparison between svm and ann
Rodrigo Moraes, JoãO Francisco Valiati, and Wilson P GaviãO Neto. 2013 · 2013
Cited alongside, same era.
The functional neural architecture of self-reports of affective experience
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Improving document-level sentiment classification using importance of sentences
Gihyeon Choi, Shinhyeok Oh, and Harksoo Kim. 2020 · 2020
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The multilingual Amazon reviews corpus
Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Ajay B Satpute, Jocelyn Shu, Jochen Weber, Mathieu Roy, and Kevin N Ochsner. 2013 · 2013
Cited alongside, same era.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Cited alongside, same era.
Inferring latent structures via information inequalities
Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, and Bernhard Schölkopf. 2014 · 2014
Cited alongside, same era.
Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
Cited alongside, same era.
Distinguishing cause from effect using observational data: Methods and benchmarks
Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf. 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.
Telling cause from effect in deterministic linear dynamical systems
Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, and Michel Besserve. 2015 · 2015
Cited alongside, same era.
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Causal direction of data collection matters: Implications of causal and anticausal learning for NLP
Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, and Bernhard Schoelkopf. 2021 · 2021
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Towards causal representation learning
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. 2021 · 2021
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Can large language models distinguish cause from effect?
Zhiheng Lyu, Zhijing Jin, Rada Mihalcea, Mrinmaya Sachan, and Bernhard Schölkopf. 2022 · 2022
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Obtaining causal information by merging datasets with MAXENT
Sergio Hernan Garrido Mejia, Elke Kirschbaum, and Dominik Janzing. 2022 · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022
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Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Original or translated? A causal analysis of the impact of translationese on machine translation performance
Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, and Bernhard Schölkopf. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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True few-shot learning with Prompts—A real-world perspective
Timo Schick and Hinrich Schütze. 2022 · 2022
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Causality for machine learning
Bernhard Schölkopf. 2022 · 2022
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Reasoning implicit sentiment with chain-of-thought prompting
Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, and Tat-Seng Chua. 2023 · 2023
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A systematic review of aspect-based sentiment analysis (ABSA): domains, methods, and trends
Yan Cathy Hua, Paul Denny, Katerina Taskova, and Jörg Wicker. 2023 · 2023
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OpenAI. 2023 · 2023
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Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research
Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, and Rada Mihalcea. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
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