Unlocking the Power of Text Analytics and Machine Learning
Discover the exponential growth in text analytics and machine learning applications, offering accountants invaluable insights from various sources like financial reports, emails, and social media. Explore how machine learning enhances prediction accuracy and efficiency in data processing, transforming unstructured text into actionable intelligence. Unleash the potential of text analytics and machine learning for advanced data analysis and decision-making.
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Presentation Transcript
Text Analytics and Machine Text Analytics and Machine Learning Workshop Learning Workshop October 26, 2018 CPA Canada - Toronto, Canada
Text analytics use is skyrocketing Text analytics use is skyrocketing More machine readable text Computing & data storage that is cheaper, more mobile, powerful & connected Machine learning More efficient, less error prone text processing More data for business intelligence = better predictions IntellyticsHub
Text of interest to accountants includes: Text of interest to accountants includes: News e.g. earnings announcements, trade publications Business documents: invoices, contracts Meeting minutes Emails Social media Annual reports (MD&A, audit report, internal control report, etc.) Proxy statements (Committee reports, director biographies) Contracts Conference call transcripts Interview transcripts
Text Analytics + Machine Learning: a powerful duo Text Analytics + Machine Learning: a powerful duo Machine learning:Builds systems that learn from data, identify patterns, and make predictions (and decisions) with minimal human intervention. Text analytics: Extracts relevant information from text by turning unstructured text into data for analysis. Techniques range from simple searches to using deep learning (a subset of machine learning) for natural language processing (NLP). Structured data SAS Institute: Hall, Dean, Kabul, and Silva: An Overview of Machine Learning
Text Analytics can Text Analytics can Visualize and describe text Crawl and tag social media, web resources, & other documents (e.g. contracts) Search words/phrases/n-grams Recognize entities Classify documents Identify topics in collections of text Cluster similar documents Extract text or concepts Analyze sentiment and tone Tag parts of speech - NLP Structure text for analysis Screen captures: Provalis Research - Wordstat
Machine Learning Machine Learning
Types of machine learning Types of machine learning
So many different models to choose from! So many different models to choose from! Simple methods may underfit Complex methods may overfit -> Poor prediction -> not generalizable Experiment to find the simplest one that predicts well enough
Choice considerations Choice considerations Complex models may handle data better Outliers Non-linearity Correlated variables Unknown interactions between variables More features than observations data-driven, not theory-driven Machine Learning is not a magic bullet Relies on good data Results biased by bias in data black box interpretation challenge Requires data science skills & infrastructure that are in short supply
Deployment challenges Deployment challenges Data security & privacy concerns Need to localize models Models degrade quickly Python, R other machine learning tools used to create the models may not be easy to put into production systems Making Netflix Machine Learning Algorithms Reliable International Conference on Machine Learning 2017
Machine Learning Gartner Hype Cycle Gartner Hype Cycle Text Analytics
What machine learning cant do YET! What machine learning can t do YET! Think infer feel create
