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The topic of aspect-based sentiment analysis (ABSA) has been explored for a variety of industries, but it still remains much unexplored in finance.
Www’18 open challenge: Financial opinion mining and question answering
Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, and Alexandra Balahur. 2018 · 1942
Earlier work this paper cites.
Fine-grained analysis of financial tweets
Chung-Chi Chen, Hen-Hsen Huang, and Hsin-Hsi Chen. 2018 · 1949
Earlier work this paper cites.
Aspect-based financial sentiment analysis using deep learning
Hitkul Jangid, Shivangi Singhal, Rajiv Ratn Shah, and Roger Zimmermann. 2018 · 1966
Earlier work this paper cites.
Inf-ufg at fiqa 2018 task 1: Predicting sentiments and aspects on financial tweets and news headlines
Dayan de França Costa and Nadia Felix Felipe da Silva. 2018 · 1971
Earlier work this paper cites.
Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, and Fernando Pereira. 2006 · 2006
Earlier work this paper cites.
Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
Earlier work this paper cites.
A survey on transfer learning
Sinno Jialin Pan, Qiang Yang, et al. 2010 · 2010
Earlier work this paper cites.
Aspect-based sentiment analysis of movie reviews on discussion boards
Tun Thura Thet, Jin-Cheon Na, and Christopher SG Khoo. 2010 · 2010
Cited alongside, same era.
Do fund managers identify and share profitable ideas?
Wesley R Gray, Steve Crawford, and Andrew E Kern. 2012 · 2012
Cited alongside, same era.
One billion word benchmark for measuring progress in statistical language modeling
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2013 · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 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.
Bjarke Felbo, Alan Mislove, Anders Søgaard, Iyad Rahwan, and Sune Lehmann. 2017 · 2017
Later among the works it cites.
Regularizing and optimizing LSTM language models
Stephen Merity, Nitish Shirish Keskar, and Richard Socher. 2017 · 2017
Later among the works it cites.
Universal language model fine-tuning for text classification
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Andrew M. Dai and Quoc V. Le. 2015 · 2015
Cited alongside, same era.
Deep learning for aspect-based sentiment analysis
Bo Wang and Min Liu. 2015 · 2015
Cited alongside, same era.
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Semeval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news
Keith Cortis, André Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, Siegfried Handschuh, and Brian Davis. 2017 · 2089
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