Evaluating sentiment in financial news articles

By Robert P. Schumaker, Yulei Zhang, Chunneng Huang, Hsinchun Chen. 2012

346 citations recorded by the source corpus.

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

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Citations in the corpus

Improving stock market prediction via heterogeneous information fusion (2017, background): “The role of author tone in financial news articles is investigated in [27], which suggests a contrarian investment manner, i.”
Decision support from financial disclosures with deep neural networks and transfer learning (2017, methodology): “The components are adapted from the systems described in [12, 8, 9, 10, 11].”
Predicting the “helpfulness” of online consumer reviews (2017, background): “The sentiments of the review have a direct impact on sales as stated by several researchers (Li &Wu, 2010; Liu et al., 2013; Schumaker et al., 2012).”
A survey of the applications of text mining in financial domain (2016, methodology): “(2013) Corporate News SVM, SVR Up/ Down Schumaker et al. (2012) US Financial News SVR Error Rate Groth and Muntermann (2011) German ad hoc Announcements NB, k-NN, SVM, NN Up/ Down Bollen et al. (2010) Tweets SOFNN Error Rate Huang et al. (2010) Financial News Weighted AR Up/ Down Li (2010) The US…”
Text mining for market prediction: A systematic review (2014, methodology): “Few of the researchers have taken advantage of these additional inputs as indicated in Table 4 (Butler & Kešelj, 2009; Hagenau et al., 2013; Rachlin et al., 2007; Schumaker & Chen, 2009; Schumaker et al., 2012; Zhai et al., 2007).”
Media-aware quantitative trading based on public Web information (2014, methodology): “apply a sentiment analysis tool named OpinionFinder to tell the (positive or negative) tone of a news article, and use such a binary sentiment result alongside the news textual vector as the input of the predictive model [32].”
Approaches, Tools and Applications for Sentiment Analysis Implementation (2015, background): “In [59], the authors have investigated the Arizona Financial Text (AZFinText) system a financial news article prediction system, and has paired it with a sentiment analysis tool.”
A Tensor-Based Information Framework for Predicting the Stock Market (2016, methodology): “We assumed zero transaction costs as in previous studies [Chan 2003; Lavrenko et al. 2000; Schumaker et al. 2012; Wang et al. 2012; Li et al. 2014a, 2014b] because the transaction costs can be absorbed by increasing the volume of each transaction provided that the transaction remains profitable.”
Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment (2015, methodology): “In short, the common approach for training and model creation in the literature (Hagenau et al., 2013; Schumaker et al., 2012; Yu et al., 2013) is some variation of the below steps: Step 1: Taking a feature-matrix as input.”
Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research (2016, methodology): “Schumaker et al. (2012) and Sinha (2010) took a similar approach, inputting sentiment analyses to the classifier.”
Tensor-Based Learning for Predicting Stock Movements (2015, neutral): “The common strategy in previous studies (Lavrenko et al. 2000; Li et al. 2014a; Mittermayer and Knolmayer 2006; Schumaker and Chen 2008; 2009b; Schumaker et al. 2012; Wang, Huang, and Wang 2011; Xu and Zhang 2013; Yu, Duan, and Cao 2013) is to concatenate features of different information sources in”
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, methodology): “Schumaker et al. (2012) built a system to evaluate the sentiment in ﬁnancial news articles using a Support Vector Regression learner with features extracted from noun phrases and scored on a positive/negative sub-jectivity scale, but the results had limited success.”
News-based trading strategies (2016, background): “Several papers [16, 17, 18] from the DSS domain elaborate on a general system design, but do not go as far as comparing approaches to trading signals and evaluating the accuracy of the resulting decisions within a ﬁnancial portfolio.”
The effect of news and public mood on stock movements (2014, methodology): “Notably, we assume zero transaction cost, as previously described [7,21,36,45].”