#QUICK WORD CLOUD GENERATOR HOW TO#

Next, drop the R Language Analysis transform that will allow you to implement the text mining using the R language. The data cube and the result look like this: In our case, the text is stored in a SQL table called. To do this, first, create a data cube and drop the table that stores the text column.

In our example, customer reviews are stored in a SQL table column, and a text mining algorithm is run on that column to obtain the word frequency. The first step in visualizing data as a Word Cloud is to analyze the text and retrieve the frequency of each word within the text. You can apply the same process to analyze data from any other source such as Twitter, Facebook, etc. In this example, we’ll use retail data stored in a SQL database and will analyze what customers have to say about a particular retail store. Note that this is not required if the native Word Cloud visualization is used in v5.0.1 onwards as it can now be created in a few clicks.

#QUICK WORD CLOUD GENERATOR HOW TO#

This blog will demonstrate how to create a Word Cloud in Dundas BI using the D3.js library. For more details on how to use this visualization, please see: A Word Cloud is an excellent option to help visually interpret text and is useful in quickly gaining insight into the most prominent items in a given text, by visualizing the word frequency in the text as a weighted list.Īs of v5.0.1, Dundas BI allows you to visualize data using the native Word Cloud visualization that displays text with color and size. As unstructured data in the form of text continues to see unprecedented growth, especially within the field of social media, there is an ever-increasing need to analyze the massive amounts of text generated from these systems. A Word Cloud or Tag Cloud is a visual representation of text data in the form of tags, which are typically single words whose importance is visualized by way of their size and color.

Quick word cloud generator