Text-Driven Forecasting is about building systems that are able to predict on the future by analyzing collection of a body of natural language documents. Often they predict numeric quantities about a certain event based on various textual sources/feeds (e.g. news, twitter, facebook, polling data, opinion blogs, financial reports, amazon reviews, economics data, etc) as input and gather information gain from aspects of sentiment analysis and subjectivity. Machine Learning algorithms that can be applied to such a domain can range from regression, deep learning, decision trees, and others.
Examples:
Predicting movie reviews using social media
Predicting opinion polls using social media
Predicting stock volatility using financial data
Predicting government elections and referendums
Predicting product sales using social media
Predicting property prices in the future
Predicting risk of a potential course of action or decision
smith whitepaper
Related Courses & Resources:
Priberam Labs
Social Media Analysis & Computational Social Science
Natural Language Processing & Social Interaction
Computational Social Science
Social & Information Network Analysis
Text as Data
NLP for Social Science
Computational Social Science
Computational Linguistics / Computational Social Science
Predicting Economic Indicators from Web Text Using Sentiment Composition
Making Predictions with Textual Contents
