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Business Intelligence and Analytics: From Big Data to Big Impact
TLDR
This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework. Expand
The information content of mandatory risk factor disclosures in corporate filings
Beginning in 2005, the Securities and Exchange Commission (SEC) mandated firms to include a “risk factor” section in their Form 10-K to discuss “the most significant factors that make the companyExpand
Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums
TLDR
Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments. Expand
Credit rating analysis with support vector machines and neural networks: a market comparative study
TLDR
A relatively new machine learning technique, support vector machines (SVM), is introduced to the problem in attempt to provide a model with better explanatory power and relative importance of the input financial variables from the neural network models. Expand
Textual analysis of stock market prediction using breaking financial news: The AZFin text system
TLDR
This research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics. Expand
Writeprints: A stylometric approach to identity-level identification and similarity detection in cyberspace
TLDR
This study proposed the use of stylometric analysis techniques to help identify individuals based on writing style, and incorporated a rich set of stylistic features, including lexical, syntactic, structural, content-specific, and idiosyncratic attributes. Expand
A framework for authorship identification of online messages: Writing-style features and classification techniques
TLDR
A framework for authorship identification of online messages to address the identity-tracing problem is developed and four types of writing-style features are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship ofonline messages. Expand
Applying authorship analysis to extremist-group Web forum messages
TLDR
A special multilingual model is developed - the set of algorithms and related features - to identify Arabic messages, gearing this model toward the language's unique characteristics and incorporated a complex message extraction component to allow the use of a more comprehensive set of features tailored specifically toward online messages. Expand
Crime data mining: a general framework and some examples
TLDR
A general framework for crime data mining is presented that draws on experience gained with the Coplink project, which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997. Expand
A framework for authorship identification of online messages: Writing-style features and classification techniques
TLDR
A framework for authorship identification of online messages to address the identity-tracing problem is developed and it is shown that the proposed approach was able to identify authors ofOnline messages with satisfactory accuracy of 70 to 95%. Expand
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