Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web

By Sanjiv R. Das, Mike Y. Chen. Management Sciences, 2007

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/2ec4c835624b7c98d7cdc95b35937a57b5d968bd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Vadlamani Ravi
2. B. Shravan Kumar
3. Jacob Zahavi
4. Tomer Geva
5. Longbing Cao

In Finance

1. Vadlamani Ravi
2. B. Shravan Kumar
3. Jacob Zahavi
4. Tomer Geva
5. Mathias Kraus
6. Stefan Feuerriegel

In Management Science and Operations Research

1. Vadlamani Ravi
2. B. Shravan Kumar
3. Jacob Zahavi
4. Tomer Geva
5. Longbing Cao
6. Mathias Kraus
7. Stefan Feuerriegel

Citations in the corpus

• Empirical evaluation of an automated intraday stock recommendation system incorporating both market data and textual news (2014, background): “Other papers from the financial literature [1,9,51,52,55] have examined the informativeness of different data sources such as news and message boards which appear in textual format.”
• A survey of the applications of text mining in financial domain (2016, background): “…Good/ Bad Tetlock et al. (2008) The US Financial News Negative words ratio Up/ Down Mahajan et al. (2008) Financial News DT+SVM (Hybrid) Up/ Down Das and Chen (2007) The US Message Postings Hybrid Classifiers Accuracy Antweiler and Frank (2004) The US Message Postings Bayes + SVM Buy/…”
• Divergence of Sentiment and Stock Market Trading (2017, unknown): “…et al., 2015), economic and financial variables (Lee et al., 1991; Baker and Wurgler, 2007; Brown et al., 2008; Firth et al., 2015), social media (Das and Chen, 2007; Bollen et al., 2011; Karabulut, 2014; Siganos et al., 2014), the weather (Saunders, 1993; Hirshleifer and Shumway, 2003), and…”
• Does Twitter matter? The impact of microblogging word of mouth on consumers’ adoption of new movies (2014, unknown): “These tweets were classified as positive and negative reviews with an accuracy level of 90.2%–higher than most other studies that use sentiment analysis to code consumer comments (e.g., Das and Chen 2007), which may be a result of the brevity of MWOM messages.”
• The Value of Crowdsourced Earnings Forecasts: THE VALUE OF CROWDSOURCED EARNINGS FORECASTS (2016, unknown): “10 An earlier literature examines opinions posted on internet message boards and chatrooms and finds little or no evidence that these opinions are value-relevant (Wysocki, 1998; Tumarkin and Whitelaw, 2001; Antweiler and Frank, 2004; Das and Chen, 2007).”