The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web
Abstract
Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises different classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values—tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Giving content to investor sentiment: The role of media in the stock market
TETLOCK, P. C. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01232.x · Cited by 4,827
On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V. N., & Chervonenkis, A. Y. (1971) · Theory of Probability & Its Applications · doi:10.1137/1116025 · Cited by 2,448
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Sentiment classification of Internet restaurant reviews written in Cantonese
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Measuring and Managing Consumer Sentiment in an Online Community Environment
Homburg, C., Ehm, L., & Artz, M. (2015) · Journal of Marketing Research · doi:10.1509/jmr.11.0448 · Cited by 196
Das, S. R. (2019) · Financial Management · doi:10.1111/fima.12297 · Cited by 184
Self-citationA Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing
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Monetary Incentive and Stock Opinions on Social Media
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Metadata
- Authors
- Das, S. R., & Chen, M. Y.
- Venue
- Management Science
- Published
- 2007-09
- Volume
- 53
- Issue
- 9
- Pages
- 1375-1388
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2048658075
- Cited by
- 1,396
- References
- 3
- FWCI
- 35.07
- Type
- journal article
- Language
- en
- Open access
- closed
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