Mining of Concurrent Text and Time Series

By Victor Lavrenko, M. Schmill, Dawn J Lawrie, Paul Ogilvie, David D. Jensen, James Allan. 2008

223 citations recorded by the source corpus.

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Citations in the corpus

Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, methodology): “Based on this relationship, machine learning approaches have been directed to predict price movements by employing classifiers given news articles [8]–[14].”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, background): “However, previous work represents news documents mainly using simple features, such as bags-of-words, noun phrases, and named entities (Lavrenko et al., 2000; Kogan et al., 2009; Luss and d’Aspremont, 2012; Schumaker and Chen, 2009).”
A survey of the applications of text mining in financial domain (2016, background): “…Reports SOM Return on Equity Fung et al. (2003) Hang Seng Index, Reuters SVM Positive/ Negative Back et al. (2001) Annual Reports SOM Error Rate Lavrenko et al. (2000) Yahoo News (biz.yahoo.com), Yahoo Stock Linear Regression Gain/ Loss Thomas and Sycara (2000) Textual data of the web GA…”
Predicting Risk from Financial Reports with Regression (2009, methodology): “Lavrenko et al. (2000b) and Lavrenko et al. (2000a) modeled inﬂuences between text and time series ﬁ-nancial data (stock prices) using language models.”
Knowledge-Driven Event Embedding for Stock Prediction (2016, background): “Stock Market Prediction There has been a line of work predicting stock markets using text information from daily news (Lavrenko et al., 2000; Schumaker and Chen, 2009; Xie et al., 2013; Peng and Jiang, 2015; Li et al., 2016).”
Deep learning for stock prediction using numerical and textual information (2016, background): “[7] employed Bag-of-Words based document representation.”
Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web (2007, background): “Lavrenko, Schmill, Lawrie, Ogilvie, Jensen, and Allen (2001) use computer algorithms to identify news stories”
A quantitative stock prediction system based on financial news (2009, background): “Keywords: Knowledge management, prediction from textual documents, quantitative funds”
Dynamic Language Models for Streaming Text (2014, background): “This can be seen an inverse of the setup considered by Lavrenko et al. (2000), where news is assumed to inﬂuence stock prices.”
Exploiting Topic based Twitter Sentiment for Stock Prediction (2013, background): “, can be analyzed to extract public sentiments to help predict the market (Lavrenko et al., 2000; Schumaker and Chen, 2009).”
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series (2010, background): “…time series of either aggregate text sentiment or good vs. bad news, including analyzing stock behavior based on text from blogs (Gilbert and Karahalios 2010), news articles (Lavrenko et al. 2000; Koppel and Shtrimberg 2004) and investor message boards (Antweiler and Frank 2004; Das and Chen 2007).”
The Stock Sonar - Sentiment Analysis of Stocks Based on a Hybrid Approach (2011, background): “…of financial news, blogs and stock message boards, as well as on stock prediction from these sources, either via sentiment classification or directly (Lavrenko et al. 2000; Das and Chen 2001; Koppel and Shtrimberg 2004; Devitt and Ahmad 2007; O'Hare et al. 2009; Schumaker and Chen 2010).”
Textual analysis of stock market prediction using breaking financial news: The AZFin text system (2009, background): “claimed a 2% return from tracking four stocks over a forty day period [12].”
Correlating financial time series with micro-blogging activity (2012, background): “[23] present a model to predict the behavior of the stock of a company using news stories related to the company.”
Semantic Frames to Predict Stock Price Movement (2013, background): “Lavrenko et al. (2000) present an approach to identify news stories that inﬂuence the behavior of ﬁnancial markets, and predict trends in stock prices based on the content of news stories that precede the trends.”