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Domain adaptation using stock market prices to refine sentiment dictionaries

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Domain adaptation using stock market prices to refine sentiment dictionaries. / Moore, Andrew; Rayson, Paul Edward; Young, Steven Eric.
Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016). European Language Resources Association (ELRA), 2016.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Moore, A, Rayson, PE & Young, SE 2016, Domain adaptation using stock market prices to refine sentiment dictionaries. in Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016). European Language Resources Association (ELRA).

APA

Moore, A., Rayson, P. E., & Young, S. E. (2016). Domain adaptation using stock market prices to refine sentiment dictionaries. In Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016) European Language Resources Association (ELRA).

Vancouver

Moore A, Rayson PE, Young SE. Domain adaptation using stock market prices to refine sentiment dictionaries. In Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016). European Language Resources Association (ELRA). 2016

Author

Moore, Andrew ; Rayson, Paul Edward ; Young, Steven Eric. / Domain adaptation using stock market prices to refine sentiment dictionaries. Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016). European Language Resources Association (ELRA), 2016.

Bibtex

@inproceedings{8600d8e82847416fb6d637d84c13013f,
title = "Domain adaptation using stock market prices to refine sentiment dictionaries",
abstract = "As part of a larger project where we are examining the relationship and influence of news and social media on stock price, here we investigate the potential links between the sentiment of news articles about companies and stock price change of those companies. We describe a method to adapt sentiment word lists based on news articles about specific companies, in our case downloaded from the Guardian. Our novel approach here is to adapt word lists in sentiment classifiers for news articles based on the relevant stock price change of a company at the time of web publication of the articles. This adaptable word list approach is compared against the financial lexicon from Loughran and McDonald (2011) as well as the more general MPQA word list (Wilson et al., 2005). Our experiments investigate the need for domain specific word lists and demonstrate how general word lists miss indicators of sentiment by not creating or adapting lists that come directly from news about the company. The companies in our experiments are BP, Royal Dutch Shell and Volkswagen.",
keywords = "Sentiment analysis, Sentiment dictionaries, Domain adaptation",
author = "Andrew Moore and Rayson, {Paul Edward} and Young, {Steven Eric}",
year = "2016",
month = may,
day = "23",
language = "English",
booktitle = "Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016)",
publisher = "European Language Resources Association (ELRA)",

}

RIS

TY - GEN

T1 - Domain adaptation using stock market prices to refine sentiment dictionaries

AU - Moore, Andrew

AU - Rayson, Paul Edward

AU - Young, Steven Eric

PY - 2016/5/23

Y1 - 2016/5/23

N2 - As part of a larger project where we are examining the relationship and influence of news and social media on stock price, here we investigate the potential links between the sentiment of news articles about companies and stock price change of those companies. We describe a method to adapt sentiment word lists based on news articles about specific companies, in our case downloaded from the Guardian. Our novel approach here is to adapt word lists in sentiment classifiers for news articles based on the relevant stock price change of a company at the time of web publication of the articles. This adaptable word list approach is compared against the financial lexicon from Loughran and McDonald (2011) as well as the more general MPQA word list (Wilson et al., 2005). Our experiments investigate the need for domain specific word lists and demonstrate how general word lists miss indicators of sentiment by not creating or adapting lists that come directly from news about the company. The companies in our experiments are BP, Royal Dutch Shell and Volkswagen.

AB - As part of a larger project where we are examining the relationship and influence of news and social media on stock price, here we investigate the potential links between the sentiment of news articles about companies and stock price change of those companies. We describe a method to adapt sentiment word lists based on news articles about specific companies, in our case downloaded from the Guardian. Our novel approach here is to adapt word lists in sentiment classifiers for news articles based on the relevant stock price change of a company at the time of web publication of the articles. This adaptable word list approach is compared against the financial lexicon from Loughran and McDonald (2011) as well as the more general MPQA word list (Wilson et al., 2005). Our experiments investigate the need for domain specific word lists and demonstrate how general word lists miss indicators of sentiment by not creating or adapting lists that come directly from news about the company. The companies in our experiments are BP, Royal Dutch Shell and Volkswagen.

KW - Sentiment analysis

KW - Sentiment dictionaries

KW - Domain adaptation

M3 - Conference contribution/Paper

BT - Proceedings of the 10th edition of Language Resources and Evaluation Conference (LREC2016)

PB - European Language Resources Association (ELRA)

ER -