And since a sentence can finish with exclamation mark or a question mark it is useful to replace ‘!’ and ‘?’ with ‘.’ Later we will use a period a separator to split opinions into sentences.Then, replace all punctuation symbols with a space.First, duplicate the Opinions column so you can have original text.Instead, here some lessons learnt from practice: I believe they are well-known to average user of Power Query so will I skip detailed operations. The cleaning can easily be done with tools like Replace Values, Trim, and Lowercase. Why do that last one? Because we want to count the most frequently used words and for Power Query ‘hotel’, ‘hotel,’, ‘Hotel’ are different words. Preparing data for analysisįirst, we load the table in Power Query (From Table) and clean it of punctuation symbols like commas, semicolons, dashes, brackets, etc., as well as replace capital letters with lower cased one. So let’s play a little within Power Query and see how it can help us analyze text. Even if you do that, you need to summarize the information somehow. Source: and īut how can you get some insights from this data? Obviously you will spend lots of time to read all reviews. * Table contains ~300 real opinions for several Bulgarian SPA hotels in Hissarya, Velingrad, Sandanski and Bansko. Thanks to Power Query, you will be able to extract, clean and shape data from those sites to receive a nice table like this: You want to analyze reviews about the hotel in sites like and to understand the major service issues. Assume you are the newly appointed Marketing Manager of a 4-star SPA hotel. Our scenarioįirst, let’s put things in a context with simple but realistic example. This is a new area for me as well so I’ll be grateful for any comments, thoughts, ideas and shared experience that will help to elaborate further on the methodology. In this article, I will give you some ideas how Power Query can be used for analyzing text that is unstructured. But this powerful tool opens doors for analysts that had been closed for long time, such as for analyzing text. We all know how good Power Query is for cleaning, shaping and analyzing numerical data.
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