Text Preprocessing

YouTube Excerpt: In this tutorial, we delve into the crucial first steps of text preprocessing for effective text mining pipelines. Using the BBC3 dataset as an example, we explore key techniques such as tokenization, stopword removal, and normalization to improve data quality. Discover how to optimize your text data for machine learning and gain insights efficiently. This video is a part of the Text Mining video series that dives into machine learning, visual analytics, and the joys of interactive analysis of text documents using Orange Data Mining software (https://orangedatamining.com). SUBSCRIBE to our channel: http://youtube.com/orangedatamining The development of this video series was supported by grants from the Slovenian Research Agency (including P2-0209, V2-2274, and L2-3170), Slovenia Ministry of Digital Transformation, European Union (including xAIM and ARISA) and Google.org/Tides foundation. #textmining #machinelearning #orange #visualanalytics #datamining __ Written by: Blaž Zupan (http://biolab.si/blaz), Ajda Pretnar Žagar Presented by: Noah Novšak Production and edit: Lara Zupan Intro/outro: Agnieszka Rovšnik Music by: Damjan Jović – Dravlje Rec Orange is developed by Biolab at University of Ljubljana (https://www.biolab.si)

In this tutorial, we delve into the crucial first steps of text preprocessing for effective text mining pipelines. Using the BBC3 dataset as an...

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