“Wordstat 9 turns the organization’s challenges into practical and effective text analysis actions distributed across the organization, no matter what the level of expertise of the employee,” says Normand Péladeau, CEO of Provalis Research. “We focused on building a user interface that can be used by non-programmers and programmers alike to expedite data mining within the organization,” adds Péladeau.
Organizations with limited programming resources can reduce the volume of programming requests by incorporating Python and R routine scripts with general purpose user interfaces to benefit from text transformations, powerful spelling corrections, unique text analysis routines, data visualization, and an unsurpassed categorization system. Wordstat 9 also increases interactivity levels through new features to analyze co-occurrence as well as the relationship between unstructured text and structured data, allowing for deeper text analysis insights.
WordStat imports information from diverse data sources with automatic data cleaning features that ensure focus on meaningful information. Apart from directly importing from MS Excel, MS Word, PDF, SPSS, Stata, social media, e-mail, and web survey platforms, version 9 also allows importation of news transcripts from the LexisNexis and Dow Jones’s Factiva output files.
WordStat is a language-independent text mining software ideal for sorting through diverse data sources. This means that the software requires no further translation of data and is now available to a greater number of researchers, particularly with the inclusion of languages such as Chinese, Japanese, Thai, Korean, etc. In order to treat text more accurately, Wordstat 9 also features a much faster and more accurate spelling correction engine. This feature allows for on-the-fly automatic spelling correction, particularly useful for analyzing social media and web survey data.
Importantly, it is now also possible to create pre- and post-processing scripts, performing custom analysis on the original or transformed text data or on quantified results obtained through content analysis of these documents. This feature offers endless possibilities to extend the capabilities of WordStat, such as implementing new machine learning algorithms, advanced statistical modeling techniques, or custom data transformation. Sample scripts have been included to compute text readability metrics, detect languages, apply other topic modeling techniques (LDA or STM), or to create predictive models using machine learning (SVM, Neural Network, Decision Tree, etc.).