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

By Sanjiv Ranjan Das, Mike Y. Chen. 2001

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Citations in the corpus

Textual Sentiment in Finance: A Survey of Methods and Models (2014, neutral): “Das and Chen (2007) use five algorithms to classify internet messages into bullish, bearish or neutral .”
Good debt or bad debt: Detecting semantic orientations in economic texts (2013, background): “Das and Chen (2007) resort to a more sophisticated approach of combining a dictionary-based approach with a complicated multiple-classiﬁer voting system, which combines word count based classiﬁers with Bayesian and Discriminant-based classiﬁers.”
Sentiment Analysis and Subjectivity (2010, methodology): “Sentiment classification is perhaps the most widely studied topic [3, 6, 8, 12, 13, 15, 16, 18, 27, 28, 33, 34, 35, 44, 45, 62, 64, 66, 67, 68, 70, 71, 73, 79, 80, 86, 92, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 111].”
Designing novel review ranking systems: predicting the usefulness and impact of reviews (2007, background): “For example, Das and Chen [5] examined bulletin boards on Yahoo! Finance to extract the sentiment of individual investors about tech companies and about the tech sector in general.”
Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “It can be used in areas of research like emotion detection in suicide notes as done by Desmet and Hoste (2013); or it can be used, for example, to determine consumer sentiment towards a brand (Mostafa, 2013). Moraes, Vasconcelos, Prado, Almeida, and Gonçalves, (2013) analyze the polarity of micro-re”
Management’s tone change, post earnings announcement drift and accruals (2010, unknown): “Others, notably, Das et al. (2005) and Das and Chen (2007), examine the association between stock price movements and online discussions and news activities using their own tone (or sentiment) index based on five distinct natural language processing algorithms that classify such discussions as bulli”
Big Data and Data Science Methods for Management Research (2016, methodology): “Also, we can refine the program by making it look at groups of words instead of single words to make it pick up on fragments such as “the financial outlook for firm A is not very positive”, which does not reflect positive news despite the use of the word positive (Das & Chen, 2007).”
Evidence on the Information Content of Text in Analyst Reports (2014, background): “…extract opinion in a wide variety of text domains, including manager opinions in financial statements (Li 2010), investor sentiments in Internet stock message boards (Das and Chen 2007; Antweiler and Frank 2004), and editorial opinions in Wall Street Journal articles (Yu and Hatzivassiloglou 2003).”
Natural language based financial forecasting: a survey (2017, methodology): “[124] Number of tuples occurrences Naı̈ve Bayes & Association Rules Experimentally tuned k-NN [59] Trend possibility distribution n-gram Language model Conditional probability maximization [40] Tf-idf weighted key words Support Vector Machine Split-and-merge segmentation [2] Text classification Regr”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “The use of sentiment analysis and related approaches has gained great popularity in the past decade due to several factors, including the advance of machine learning methods in natural language processing and information retrieval, the availability of large and rich datasets for machine learning alg”
Mine Your Own Business: Market-Structure Surveillance Through Text Mining (2012, background): “Collaboration between computer scientists and business researchers has often facilitated the dissemination (albeit limited) of these tools to business research (e.g., Das and Chen 2007; Feldman et al. 2007, 2008; Lee and Bradlow 2011).”