WordStat is a

content analysis Content analysis is the study of documents and communication artifacts, known as texts e.g. photos, speeches or essays. Social scientists use content analysis to examine patterns in communication in a replicable and systematic manner. One of the ...

and

text mining Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from differe ...

software. It was first released in 1998 after being developed by Normand Peladeau from

Provalis Research Provalis Research is a Canadian company that specializes in developing and marketing text analytics tools combining qualitative analysis through QDA Miner with quantitative content analysis and text-mining through WordStat. Headquartered in Mon ...

. The latest version 9 was released in 2021. The software is mainly used for business intelligence and competitive analysis of web sites,

sentiment analysis Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subje ...

of open-ended questions, theme extraction from social media data, etc.

Some features of WordStat 9

* Categorization of content using user defined dictionaries. * Classification of documents using Naïve-Bayes or k-nearest neighbor algorithms applied either on words or concepts. * Automatic topic extraction using first order (word co-occurrences) or second order (co-occurrence profiles) hierarchical clustering and multidimensional scaling. * Topic modeling to extract the main themes using NNMF and Factor Analysis. *

Correspondence analysis Correspondence analysis (CA) is a multivariate statistical technique proposed by Herman Otto Hartley (Hirschfeld) and later developed by Jean-Paul Benzécri. It is conceptually similar to principal component analysis, but applies to categorical ...

in order to identify words or concepts (or content categories) associated with any categorical meta-data associated with documents. * Pre-and post-processing with R and python script * Analyze more than 70 languages including Chinese, Japanese, Korean, Thai. * Interactive word clouds and word frequency tables can now be obtained directly on keyword retrieval and keyword-in-context (KWIC) results allowing one to quickly identify words associated with specific content categories, or those appearing, before, after a specific target item. * Relate unstructured text with structured data such as dates, numbers or

categorical data In statistics, a categorical variable (also called qualitative variable) is a variable (research), variable that can take on one of a limited, and usually fixed, number of possible values, assigning each individual or other unit of observation to a ...

for identifying temporal trends or differences between subgroups or for assessing relationship with ratings or other kind of categorical or numerical data. * Visualization tools to visualize and interpret text analysis results: **Dendrogram with optional bar chart **2D and 3D Multidimensional scaling **Proximity plot **Heatmap (with dual clustering) **Bubble chart **Bar chart, pie chart, line chart, word clouds **Correspondence plots (2D and 3D)

References

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