If you are looking to conduct sentiment analysis on textual data in Power BI with the help of text analysis application programming interfaces, here is a procedure you can follow:
Do the Data Preparation: First and foremost, gather the text you want to analyze and store it logically. For example, use Excel, CVS, or a database. Do Away With Unwanted Extras: Make the files neat by eliminating all the unneeded elements, such as special symbols and non-critical areas of information, so they are cleaner for analysis.
Please bring in the Text Analytics API: There are many Text Analytics APIs available; you can select one of them, such as Microsoft Azure Text Analytics or Google Cloud Natural Language API. Create a profile and generate an API key for verification before accessing the Text Analytics API. In Power BI, HTTP POST requests are made to the API using Power Query. For example, in Power Query, M code can be written to send batches of text to the API, get back the sentiment scores, and add the scores to the existing dataset.
Analyze the API Responses: Use the sentiments and other useful analyses within the API return on power query. These scores may be added to your data set for further exploration in new sections.
Impressions of Sentiment Assessment: Once you have the sentiment scores, display the trends using other visual elements of Power BI, such as bar charts, line graphs, heat maps, and so on. Also, include slicers or filters to view a particular period, customer group, or category of feedback.
Execute and Update Strategy: In Power BI, create several refresh schedules that will enable you to always have the latest analysis. Ensure that API requests are made economically to avoid being cut off.
In addition to the custom strategy mentioned, which will help you save time formatting the text data, effective visualization will allow you to make sense of the text data and even bring out the trends in sentiments effectively.
