Fetching the paper…
Reading the bibliography…
In the financial domain, conducting entity-level sentiment analysis is crucial for accurately assessing the sentiment directed toward a specific financial entity.
Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
Earlier work this paper cites.
Finbert: A pretrained language model for financial communications
Yi Yang, Mark Christopher Siy Uy, and Allen Huang. 2020 · 2006
Earlier work this paper cites.
Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters
Kilem L Gwet. 2014 · 2014
Earlier work this paper cites.
Good debt or bad debt: Detecting semantic orientations in economic texts
Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wallenius, and Pyry Takala. 2014 · 2014
Earlier work this paper cites.
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 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
seqeval: A python framework for sequence labeling evaluation
Hiroki Nakayama. 2018 · 2018
Earlier work this paper cites.
Nowcasting euro area gdp with news sentiment: a tale of two crises
Julian Ashwin, Eleni Kalamara, and Lorena Saiz. 2021 · 2021
Cited alongside, same era.
Impact of news on the commodity market: Dataset and results
Ankur Sinha and Tanmay Khandait. 2021 · 2021
Cited alongside, same era.
Buy tesla, sell ford: Assessing implicit stock market preference in pre-trained language models
Chengyu Chuang and Yi Yang. 2022 · 2022
Cited alongside, same era.
Disclosure sentiment: Machine learning vs. dictionary methods
Richard Frankel, Jared Jennings, and Joshua Lee. 2022 · 2022
Cited alongside, same era.
Sentfin 1.0: Entity-aware sentiment analysis for financial news
Ankur Sinha, Satishwar Kedas, Rishu Kumar, and Pekka Malo. 2022 · 2022
Cited alongside, same era.
A robust textual analysis of the dynamics of hong kong property market
Ken Wong, Max Kwong, Paul Luk, and Michael Cheng. 2022 · 2022
Analyzing firm reports for volatility prediction: A knowledge-driven text-embedding approach
Yi Yang, Kunpeng Zhang, and Yangyang Fan. 2022 · 2022
Later among the works it cites.
Finbert: A large language model for extracting information from financial text
Allen H Huang, Hui Wang, and Yi Yang. 2023 · 2023
Closest in time.
Closed ai models make bad baselines
Anna Rogers, Niranjan Balasubramanian, Leon Derczynski, Jesse Dodge, Alexander Koller, Sasha Luccioni, Maarten Sap, Roy Schwartz, Noah A Smith, and Emma Strubell. 2023 · 2023
Closest in time.
Financial numeric extreme labelling: A dataset and benchmarking for xbrl tagging
Soumya Sharma, Subhendu Khatuya, Manjunath Hegde, Afreen Shaikh Dasgupta, Pawan Goyal, Niloy Ganguly, et al. 2023 · 2023
Closest in time.
Investlm: A large language model for investment using financial domain instruction tuning
Yi Yang, Yixuan Tang, and Kar Yan Tam. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Trillion dollar words: A new financial dataset, task & market analysis
Agam Shah, Suvan Paturi, and Sudheer Chava. 2023a
Cited in the paper.
Finer: Financial named entity recognition dataset and weak-supervision model
Agam Shah, Ruchit Vithani, Abhinav Gullapalli, and Sudheer Chava. 2023b
Cited in the paper.
Closest in time.
Evaluating sentiment analysis in the context of securities trading
Siavash Kazemian, Shunan Zhao, and Gerald Penn. 2016 · 2094
Closest in time.