![]() ![]() Thu 14 Jul 9:00 - 12:30 Unsupervised methods for textual analysisġ4:30 -18:00 Application: Latent Dirichlet Allocation on Wikipediaįri 15 Jul 9:00 - 12:30 Paper presentationġ4:30 -18:00 Advanced methods in text mining Wed 13 Jul 9:00 - 12:30 Sentiment Analysisġ4:30 -18:00 Application: Measuring well-being on Twitter Tue 12 Jul 9:00 - 12:30 Natural Language Processingġ4:30 -18:00 Application: NLP to analyse central bank Mon 11 Jul 9:00 - 12:30 Introduction to Pythonġ4:30 -18:00 Application: How to get data from API and websites We will also offer participants the opportunity to present their research and/or projects, and if possible, we will assist them with their projects - both on the data collection side and on the data analysis side. We will then study and share with the participants’ scripts and codes to realize different tasks in Python. Finally, the last session will be devoted to advanced methods of textual analysis to open the field of possibilities by introducing different methods of machine learning, word embedding and data structuring.įor the different sessions, we will first present both the related theories and methods - in a language accessible to non-mathematicians - and their latest applications in the economic and financial literature. We will perform an application of a Latent Dirichlet Allocation on a large corpus of Wikipedia articles. Then, we will introduce the unsupervised methods of textual analysis with a particular focus on topic modelling methods. We will analyse Twitter data to build a sentiment indicator capturing the well-being of individuals in a country. The next session will be dedicated to sentiment analysis and will present the different methods (dictionary approach and machine learning). We will apply this to the speeches made by the European Central Bank to show how it is possible to give structure to unstructured data. Next, we will see how to analyse a text using Natural Language Processing (NLP) methods. We will create an application to collect articles from a major media site and we will use an API to extract tweets from a social network dedicated to finance. After an introduction to the Python programming language, we will start by seeing how it is possible to extract online content via the use of existing APIs or the implementation of web scraping tools. The objective of this course is study how we can use the millions of textual contents published on the Internet and social media every day to improve our understanding of various economic and financial phenomena. Natural language processing with Python: analyzing text with the natural language toolkit. Applied text analysis with python: Enabling language-aware data products with machine learning. Web scraping with Python: Collecting more data from the modern web. Journal of Business & Economic Statistics, 38(2), 393-409. Words are the new numbers: A newsy coincident index of the business cycle. Intraday online investor sentiment and return patterns in the US stock market. Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages. Textual analysis in accounting and finance: A survey. Journal of International Money and Finance, 79, 136-156. Words are not all created equal: A new measure of ECB communication. Journal of International Money and Finance, Forthcoming. Media sentiment on monetary policy: determinants and relevance for inflation expectations. Picault, M., Pinter, J., & Renault, T.International Review of Financial Analysis, 33, 171-185. Textual sentiment in finance: A survey of methods and models. Journal of Public Economics, 191, 104274. Economic uncertainty before and during the COVID-19 pandemic. It is possible to follow the tutorial available at to learn or review the basics of programming in Python. Participants must install Anaconda ( ) to have a functional programming environment before the beginning of the course. ![]() The maximum number of allowed participants in presence is 30.īasic knowledge of statistics. Participants should have a basic understanding of computer programming. The activation of the course in presence is conditional to the recruitment of a minimum of 15 participants. Matthieu Picault, University of Orléans (France) and Laboratoire d’Économie d’Orléans.Textual analysis and machine learning with applications to economics and finance Perugia, 11-15 July 2022Įmail: Thomas Renault, University Paris 1 Panthéon-Sorbonne (France) ![]()
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