Text as Alternative Data: An Introduction with Forecasting Applications, May 8-19
Online courses
Certificate of Attendance
Regular fees: 1100 - 1950 EUR
10% early-bird discount applies to confirmation payments made on or before April 7, 2023
Reduced Fee applies for Ph.D. or Master's students, Alumni of BSE Master's programs, and participants who are unemployed.
Groups and other discounts are available, contact a BSE Admissions Counselor to request your personalized quotation.
Lupe Castro, Admissions Office
Text mining is a rapidly growing field within data science that involves analyzing large amounts of text data to extract valuable insights and information. Text is an "alternative data source" that can be processed to produce data from a variety of sources including social media, news articles, and customer reviews. By analyzing this data, organizations can gain a better understanding of their customers, track trends and sentiment, identify themes and topics, and make more informed decisions. This makes text mining skills valuable in a variety of sectors including marketing, finance, healthcare, education, and government.
Get to know how text mining is used in larger pipelines that provide concrete decision-making support
This 20h online course will enable you to integrate textual data into your work environment after only two weeks of training. You will be able to use dictionary-based sentiment analysis for stock market evaluations, use the topic model to conduct political risk predictions, and use BERT to conduct sentiment analysis on financial text.
After successful completion of the course, you will:
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Understand text pre-processing
Text pre-processing is an essential step in the text mining process, as it prepares the text data for further analysis. By learning text pre-processing techniques such as tokenization and stemming, you will be able to effectively clean and prepare text data for analysis. -
Have learnt LDA
Latent Dirichlet Allocation (LDA) is a popular technique for topic modeling that allows you to identify the main themes or topics present in a collection of documents. This can be useful for text summarization, document classification, and information retrieval. -
Understand sentiment analysis
Sentiment analysis is the process of identifying the sentiment expressed in a piece of text, whether it be positive, negative, or neutral. By learning sentiment analysis techniques, you will be able to extract valuable insights and information from large amounts of text data. -
Have learnt BERT
BERT is a state-of-the-art transformer-based language model that has achieved impressive results on a variety of natural language processing tasks. By learning about BERT, you will be able to use this powerful tool to perform tasks such as text classification and language translation.
Get the tools to effectively analyze and extract insights from large amounts of text data, a valuable skill in today's data-driven world
This course is specifically aimed at:
Professionals in all sectors (finance, consultancy, industry, public sector, international organizations) are in contact with large amounts of documents/text but donβt know how to access the information in these documents in an automated way.
People that want to predict outcomes or track sentiment towards a product or stock and have access to text sources that can be matched to these targets.
The target participant must have knowledge of Python and machine learning but can be from any academic or working background.
You can find the full course outline and more details on the course page
Ramon Trias Fargas 25-27
08005 Barcelona , Spain