Literature Review: Yahoo! for Amazon

Das and Chen (2007)1 is an important study for sentiment analysis and very typical because it’s interdisciplinary of finance and computer science. I’ll take use of backward and forward reference searching2 to do a literature review on this study. I define the backward search and forward search as follow:

  • Forward: The papers that have cited this study;
  • Backward: The papers that this study has cited;

Forward Search

Management Science

  1. Archak, Nikolay, Ghose, Anindya, Ipeirotis, Panagiotis G (2011). Deriving the pricing power of product features by mining consumer reviews. Management science, 57(8), 1485—1509

CS/EE

  1. Pang, Bo, Lee, Lillian, others, (2008). Opinion mining and sentiment analysis. Foundations and Trends{\textregistered} in Information Retrieval, 2(1—2), 1—135
  2. Liu, Bing (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1—167
  3. Dave, Kushal, Lawrence, Steve, Pennock, David M (2003). Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. , (), 519—528
  4. O’Connor, Brendan, Balasubramanyan, Ramnath, Routledge, Bryan R, Smith, Noah A (2010). From tweets to polls: Linking text sentiment to public opinion time series.. ICWSM, 11(122-129), 1—2

Finance

  1. Antweiler, Werner, Frank, Murray Z (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259—1294
  2. Tetlock, Paul C, Saar-Tsechansky, Maytal, Macskassy, Sofus (2008). More than words: Quantifying language to measure firms’ fundamentals. The Journal of Finance, 63(3), 1437—1467

IS

  1. Forman, Chris, Ghose, Anindya, Wiesenfeld, Batia (2008). Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets. Information Systems Research, 19(3), 291—313
  2. Goh, Khim-Yong, Heng, Cheng-Suang, Lin, Zhijie (2013). Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information Systems Research, 24(1), 88—107
  3. Luo, Xueming, Zhang, Jie, Duan, Wenjing (2013). Social media and firm equity value. Information Systems Research, 24(1), 146—163
  4. Yu, Yang, Duan, Wenjing, Cao, Qing (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 55(4), 919—926
  5. Schumaker, Robert P, Zhang, Yulei, Huang, Chun-Neng, Chen, Hsinchun (2012). Evaluating sentiment in financial news articles. Decision Support Systems, 53(3), 458—464
  6. Bai, Xue (2011). Predicting consumer sentiments from online text. Decision Support Systems, 50(4), 732—742
  7. Nassirtoussi, Arman Khadjeh, Aghabozorgi, Saeed, Wah, Teh Ying, Ngo, David Chek Ling (2014). Text mining for market prediction: A systematic review. Expert Systems with Applications, 41(16), 7653—7670

Backward Search

Finance

  1. Choi, J. J., Laibson, D., & Metrick, A. (2000). Does the internet increase trading? Evidence from investor behavior in 401 (k) plans (No. w7878). National Bureau of Economic Research.
  2. Wysocki, P. D. (1998). Cheap talk on the web: The determinants of postings on stock message boards.
  3. Lavrenko, V., Schmill, M., Lawrie, D., Ogilvie, P., Jensen, D., & Allan, J. (2000, August). Mining of concurrent text and time series. In KDD-2000 Workshop on Text Mining (Vol. 2000, pp. 37-44).
  4. Bagnoli, M., Beneish, M. D., & Watts, S. G. (1999). Whisper forecasts of quarterly earnings per share. Journal of Accounting and Economics, 28(1), 27-50.
  5. Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 59(3), 1259-1294.
  6. Tumarkin, R., & Whitelaw, R. F. (2001). News or noise? Internet postings and stock prices. Financial Analysts Journal, 57(3), 41-51.
  7. Das, Sanjiv, Mart{\’\i}nez-Jerez, As{\’\i}s, Tufano, Peter (2005). eInformation: A clinical study of investor discussion and sentiment. Financial Management, 34(3), 103—137

CS/EE

  1. Koller, D., & Sahami, M. (1997). Hierarchically classifying documents using very few words. Stanford InfoLab.
  2. Chakrabarti, S., Dom, B., & Indyk, P. (1998, June). Enhanced hypertext categorization using hyperlinks. In ACM SIGMOD Record (Vol. 27, No. 2, pp. 307-318). ACM.
  3. Chakrabarti, S., Roy, S., & Soundalgekar, M. V. (2003). Fast and accurate text classification via multiple linear discriminant projections. The VLDB Journal—The International Journal on Very Large Data Bases, 12(2), 170-185.
  4. Chakrabarti, S., Dom, B., Agrawal, R., & Raghavan, P. (1998). Scalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies. The VLDB Journal—The International Journal on Very Large Data Bases, 7(3), 163-178.

Recommended Reading:

Data Science: Theories, Models, Algorithms, and Analytics by S.Das


1. Das, Sanjiv R, Chen, Mike Y (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management science, 53(9), 1375—1388
2. Guide to Science Information Resources: Backward & Forward Reference Searching
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