Creating Word Cloud for Tweets in Power BI Desktop
Keywords: Data Visualization, Advanced Data Analytics, Power BI, Word Cloud, Power Bi Training, Power BI Australia, Power BI Sydney, Graphs, Dataset, Visualizations.
Blog Post:
Power BI is a powerful platform that provides an amazing support for creating various kinds of charts and graphs. The most exciting feature of Power BI is that it allows its users to create engaging charts without digging deeper into the coding world.
Businesses may use visualization (such as maps, graphs, and infographics) to convey essential information at a glance. This is useful if the data contains numeric values.
But what if the raw data is text-based? The answer is word cloud.
A word cloud is a beautiful custom visual for visualizing the key words in data. It can express vital details quickly. Moreover, the density of the words occurring inside text data can be visualized by the size of the word.
There are other resources available in the industry that can help to analyze the data and grasp it quantitatively. However, Word clouds are a low-cost and an efficient option amongst all available options.
Where Do Word Clouds Exist?
- Collecting input and feedbacks from your customers. Analyzing customer reviews will help you re-define your business strategy.
- Knowing how your staff feel about your business is crucial. Text cloud analysis will turn employee input from a pile of data to something you can use right away.
- Creating a list of new SEO words to goal. In addition to traditional keyword analysis methods, a word cloud will help you identify possible keywords to pursue.
Before you begin working with this visualization, you must first:
- Make sure the data set you are working with is text-based and context-aware.
- Quoting any text into a word cloud generator cannot provide you with the precise insights you need.
This blog will show you how to make a word cloud in Power BI desktop using the tweets about depression.
Let’s take a look at the data set we’ll be working with in this article.
To collect the tweets the python library twint has been used which scrapes the tweets on the runtime. The key word used to scrape the tweets is ‘depression’. So all the tweets that are being posted about depression are stored in the excel file.
The python code used to scrape the tweets is as follows:
The retrieved .csv file is shown below:
Now, Let’s Move Towards the Step Performed To Create The Word Cloud:
- If the word cloud is not present in the visualization tab than click on the get more visuals tab. Now click on the add button that is present in front of word cloud.
Finally, World Cloud will be visible.
2. Click on the world cloud and an empty chart will be visible on your screen.
3. Now Import the dataset previously scrapped from twitter. And select the tweets field in the Fields section. And an initial chart with default settings will be created as follows:
Word Cloud Formatting Options Provided by Power Bi Desktop:
- General: Setting the minimum of number of repetitions allowed to 3.
- Data colors
- Stop word: Turn the stop words on to avoid the commonly used words.
- Rotate text: Setting the minimum and maximum angle to 0 to avoid skewed words.
- Performance
- Title
- Background: Setting the background color to black.
Word Cloud Filter Options Provided By Power Bi Desktop:
Three types of available filtering are
- Basic Filtering
- Advanced Filtering
- Top N
For the tweets word cloud Advanced filtering with conditional arguments is set as shown.
After Optimizing the Chart With Given Options And Resultant World Cloud Given Below:
Conclusion:
The size of words and phrases in your tweet cloud is proportional to how often they appear. They are smart because they let you do a fast and easy analysis of your Twitter data and see what’s being discussed the most frequently in your feed. Word clouds are a pleasant visualization tool being used in blog posts to highlight the keywords you are concentrating on. Only bigger, clearer phrases will be seen by your readers, and they will understand their significance to your message.
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