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Word Cloud Chart

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A Word Cloud chart is a visual representation of text data in which the importance or frequency of individual words is represented using font size and color. The more important or frequently used a word is, the larger and more brightly colored it appears. This format allows users to spot the most important or frequently used words in the DataSet. This type of chart is also known as a "tag cloud."

A Word Cloud chart requires two columns or rows of data from your DataSet—one for the words in the cloud and another with values representing each word. These values are usually based on the number of occurrences of each word in a specific DataSet.

Values associated with words can also be based on other measures of importance. For example, you could create a Word Cloud chart in which each word was the name of a country and the value for each word was the country's GDP. In the resulting chart, countries with a high GDP would stand out more than those with a low GDP.

If more than one occurrence of a word appears in your DataSet, the values for those occurrences are aggregated according to the type of aggregation you select. For example, if your DataSet contains three occurrences of the word "blue" and you select Sum as the aggregation type, the three values for "blue" are added together. If you select No Aggregation, the value for the first occurrence of each word in your data is used. For more information about aggregating data, see Aggregating your data.

The top 5 words in a Word Cloud appear in orange, the next 5 appear blue, and the rest will be gray.

Powering a Word Cloud chart

In the Analyzer, you choose the columns containing the data for your Word Cloud chart. For more information about choosing data columns, see Applying DataSet Columns to Your Chart.

For more information about formatting charts in the Analyzer, see KPI Card Building Part 2: The Analyzer.

The following graphic shows you how the data from a typical column-based spreadsheet is converted into a Word Cloud chart. This chart shows the frequency of the words used most often in Ronald Reagan's 1985 State of the Union address.

Creating a Word Cloud chart from columns with phrases 

There may be times when you need to build a Word Cloud chart using a column that contains phrases as opposed to individual words. By default, the chart will include instances of entire phrases instead of breaking of them up into words. You can build a DataFlow that will allow you to output the individual words when using a Word Cloud chart. This must be done using an SQL DataFlow; it cannot be done using ETL.

To create a DataFlow for use in a Word Cloud,

  1. Create a DataFlow and add the DataSet with the column you want to parse as an input DataSet. 
    For more information about creating DataFlows, see Creating an SQL DataFlow.

  2. Create a new transform.

  3. Copy and paste the content from the following SQL file into the new transform:

  4. Deselect Generate Output Table in the transform.

  5. Click Run SQL.

  6. After the run has completed, click Apply.

  7. Create a new transform.

  8. Copy and paste the content from the following SQL file into the new transform:

  9. Deselect Generate Output Table in the transform.

  10. Edit the statement to include the name of your table, the column that needs to be split and indicators for whether or not common words and symbols should be excluded. This call is formatted as follows:

    CALL word_cloud('your_data_table', '`your_column`' , 'exclude common words (y/n)','exclude common symbols (y/n)' );

    where your_data_table is the name of the DataSet and your_column is the column you are parsing, and you insert y to exclude common words/common symbols or n to keep them.

    For example, if your table is named “survey_results” and the column to be split is called “feedback” and you’d like to exclude common words but not common symbols, you would modify the code to look like the following:

    CALL word_cloud('survey_results', '`feedback`' , 'y','n’ );

  11. Deselect Generate Output Table in the transform.

  12. Click Run SQL.

  13. After the run has completed, click Apply.

  14. Create an output DataSet in which you use a query like the following to include the results from the previous steps in the output:

    SELECT * FROM final

A new table called “final” is created, which contains the data from your original table with rows created for each item in the column to be split. There will be a new column added to the end of each record in the table that contains the original string that was split.

Common words

If you choose to remove common words the words that will be removed are as follows:

'1', '2', '3', '4', '5', '6', '7', '8', '9', '-', '.', '&', 'A', 'ABLE', 'ABOUT', 'ABSOLUTELY', 'AFTER', 'AGAIN', 'ALL', 'ALONG', 'ALSO', 'ALWAYS', 'AM', 'AN', 'AND', 'ANOTHER', 'ANY', 'ANYONE', 'ANYTHING', 'ANYWHERE', 'ARE', 'AROUND', 'AS', 'ASKED', 'AT', 'AWAY', 'BACK', 'BE', 'BECAUSE', 'BECOME', 'BEEN', 'BEFORE', 'BEST', 'BETTER', 'BETWEEN', 'BIGGEST', 'BOTH', 'BRING', 'BUT', 'BY', 'CAME', 'CAN', 'CAN''T', 'CANNOT', 'CANT', 'CHANCE', 'COME', 'COMES', 'COULD', 'COULDN''T', 'DEFINITELY', 'DID', 'DIDN''T', 'DO', 'DOES', 'DOESN''T', 'DOING', 'DON''T', 'DONE', 'DURING', 'EACH', 'ELSE', 'ENOUGH', 'ENTIRE', 'ESPECIALLY', 'EVEN', 'EVER', 'EVERY', 'EVERYDAY', 'EVERYONE', 'EVERYTHING', 'FELT', 'FEW', 'FIRST', 'FOR', 'FROM', 'FRONT', 'GET', 'GETS', 'GETTING', 'GIVE', 'GIVEN', 'GIVES', 'GO', 'GOES', 'GOING', 'GOOD', 'GOT', 'GREAT', 'HAD', 'HAS', 'HASN''T', 'HAVE', 'HAVEN''T', 'HAVING', 'HE', 'HE''S', 'HELPED', 'HER', 'HER.', 'HERE', 'HERSELF', 'HERSELF.', 'HI', 'HIM', 'HIS', 'HOW', 'I', 'I''D', 'I''LL', 'I''M', 'I''VE', 'IF', 'IM', 'IN', 'INTO', 'IS', 'IT', 'IT.', 'IT''S', 'ITS', 'JUST', 'KEEP', 'KEEPS', 'KNOW', 'LAST', 'LET', 'LIKE', 'LOOKS', 'LOT', 'MADE', 'MAKE', 'MAKES', 'MAKING', 'MANY', 'MATTER', 'MAY', 'ME', 'ME.', 'MEANS', 'MORE', 'MORE.', 'MOST', 'MUCH', 'MY', 'MYSELF', 'NEARLY', 'NEED', 'NEEDS', 'NEVER', 'NEXT', 'NO', 'NOT', 'NOTHING', 'NOW', 'OF', 'ON', 'ONE', 'ONLY', 'OR', 'OTHER', 'OTHERS', 'OUR', 'OUT', 'OVER', 'OWN', 'PLEASE', 'PROBABLY', 'PUT', 'PUTS', 'REALLY', 'RECENTLY', 'SAID', 'SAME', 'SAW', 'SAY', 'SEE', 'SEEN', 'SHE', 'SHE''S', 'SHOULD', 'SIMPLE', 'SINCE', 'SO', 'SOME', 'SOMEONE', 'SOMETHING', 'SOMEWHERE', 'SPECIAL', 'STILL', 'SUCH', 'SURE', 'SURELY', 'TAKE', 'TAKES', 'TELL', 'TH', 'THAN', 'THAT', 'THAT''S', 'THE', 'THEIR', 'THEM', 'THEN', 'THERE', 'THESE', 'THEY', 'THING', 'THINGS', 'THINK', 'THINKING', 'THIS', 'THOSE', 'THOUGH', 'THOUGHT', 'THREE', 'THROUGH', 'TILL', 'TO', 'TOGETHER', 'TOLD', 'TOO', 'TOOK', 'TOWARDS', 'TRULY', 'TRYING', 'U', 'UNTIL', 'UP', 'UR', 'US', 'US.', 'USE', 'VERY', 'VIA', 'WANT', 'WANTED', 'WANTS', 'WAS', 'WAY', 'WE', 'WE''RE', 'WENT', 'WERE', 'WHAT', 'WHATEVER', 'WHEN', 'WHENEVER', 'WHERE', 'WHICH', 'WHILE', 'WHO', 'WHOM', 'WHY', 'WILL', 'WITH', 'WITHIN', 'WITHOUT', 'WOULD', 'YET', 'YOU', 'YOU.', 'YOU''D', 'YOU''RE', 'YOUR',

Common symbols

If you choose to remove common symbols the symbols that will be removed are as follows:

'~', '`', '!', '@', '#', '£', '€', '$', '¢', '¥', '§', '%', '°', '^', '&', '*', '(', ')', '-', '_', '+', '=', '{', '}', '[', ']', '|', '\', '/', ':', ';', ''', ',', '<', '>', '.', '?', '“', '”', '-', '–', '’', '"',