How to Clean Text Data on Python (Code-along) - Natural Language Processing (NLP) for Finance

Stay Ahead in Finance. Get access to the resources for this video, plus ALL our rigorous finance and investing courses. Finally crack the code to successful investing: https://www.ferventlearning.com/all-access-pass/ Explore how to clean text data in Python working with financial text data (MD&A filing extracted from a 10-K Annual Report). 👇Timestamps and more. This video is part of our course on Investment Analysis with Natural Language Processing (NLP). You can enroll on the course here: https://www.ferventlearning.com/courses/investment-analysis-with-natural-language-processing-nlp/ What you get: ✅ 8 hours of on-demand HD video lessons that give you a solid foundation in natural language processing (NLP) applied to Investment Analysis ✅ Insanely detailed hands-on code-along walkthroughs that teach you how to work with large datasets from the ground up - from scratch - including where to get data, how to clean it, and how to really know it, inside out ✅ A system that teaches you how to transform your investment idea/thesis into a testable hypothesis (even if you don't know what a "testable hypothesis" is) ✅ Step-by-step videos, quizzes, code notebooks, and assignments that'll help you learn and master how to quantify sentiment from scratch ✅ The same research-backed, high-quality content you've come to trust and expect from Fervent Timestamps: 00:00 - Intro 00:12 - Recap of the Text Cleaning Process 01:05 - Setup code walkthrough 03:11 - Choosing a file randomly 04:28 - Extracting text from a .txt file on Python 06:25 - Exploring the text data 06:38 - Cleaning text data walkthrough 08:30 - Removing symbols, numbers, dates, etc 10:12 - Harmonising letter case 11:23 - Removing stopwords 15:20 - Impact of Text Cleaning 16:25 - Summary #naturallanguageprocessing #nlpfinance #nlp #textualanalysis #onlinecourse #finance #investment #stocks #sentiment #analysis #sentimentanalysis #cleantext #dataprocessing