![]() ![]() Sample scripts have been included to compute text readability metrics, detect languages, apply other topic modeling techniques (LDA or STM) or create predictive models using machine learning (SVM, kNN, etc.).Ī new spell-checking engine has been written from scratch to achieve much faster and more accurate spelling corrections, allowing the implementation of an automatic spelling correction feature with minimal impact on the existing text processing speed of WordStat. Such a feature offers endless possibilities to extend the features of WordStat such as implementing new machine learning algorithms, advanced statistical modeling techniques, or custom data transformation. More importantly, it is now possible to create post-processing scripts in those two programming languages allowing one to perform custom analysis on the original or transformed text data or on quantified results obtained through content analysis on those documents. Version 9.0 extends this capability by offering the possibility to create preprocessing scripts in R as well. In 2018, we introduced the possibility to create Python preprocessing scripts to WordStat 8. Integration of R and Python Pre- and Post-Processing Scripts Word segmentation routines for the previous three Asian languages have also been added.Ģ. The new Unicode version of WordStat allows one to analyze any of these without any setting changes as well as new languages previously not supported such as Chinese, Japanese, or Thai. And while it was possible to analyze datasets in multiple languages, some combinations of languages were simply not possible. ![]() However, to analyze languages not supported by their default Windows installation, the user needed to change some Windows settings. This has allowed users to analyze text data in more than 50 languages. We always try to select language-independent text analytics techniques. ![]() THE NEW FEATURES OF WORDSTAT TEXT MINING SOFTWARE What’s New in Version 9.0? 1. ![]()
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