An Introduction to Sentiment Analysis in MicroStrategy
Sentiment Analysis is becoming one of the biggest trends in data analytics today, generating demand at a continuous pace. Consultant Christian Ott provides an introduction to Sentiment Analysis in MicroStrategy, what it is, how it works, and potential use cases for it.
What is Sentiment Analysis?
For anyone who is unfamiliar with the topic, Sentiment Analysis is the process of interpreting text or speech in order to quantify the emotions present in the text. To give an example, let’s say you’re a manager of a large restaurant, and you need to go through the customer reviews you’ve received in the past week on your website. While reading through them, you might ask yourself, “which day of the week are we getting the most positive feedback” or, “how many of these reviews mention long line complaints?” Rather than tallying these reviews yourself, you can perform a Sentiment Analysis to identify key words contained in the text, as well as the overall emotion of the comment. This technique isn’t just useful for Sentiment Analysis, but useful anytime you need to perform a quantitative analysis of text. So how does it all work?
Sentiment Analysis in MicroStrategy
To get started in MicroStrategy, make sure you have the R integration package downloaded to your server or desktop (depending on which one you use). Once that’s ready, import a data set containing an attribute for the text you want to analyze. Next, create a new R-Script metric using the Sentiment Analysis library in the formula editor. This allows you to start calling functions from the library, and inputting them as variables to add to your visualizations. Some of these potential metrics include the emotion score of the text, the overall sentiment score, or the number of words in the text that are contained in the library. If you are new to Sentiment Analysis or using R-Scripts in MicroStrategy, running into challenges is expected. While these shouldn’t be permanent fixes, here are some ways you can mitigate common issues to get started:
Even with little experience in Sentiment Analysis, you should still end up with some pretty powerful findings. For example, if you’re looking at customer reviews associated with the store visited, you can compare average sentiment scores, or commonly used words across different stores, regions, and markets in your company. In addition, if you have access to operations data, you could even compare average scores to net sales of each store. You can also use MicroStrategy’s built in data connection with Twitter to analyze how your company is perceived on social media. You could begin to identify trending words used in tweets and the sentiments surrounding those words. While you can’t gain much twitter data regarding operations, it could be useful for marketing teams.
The impact that an analysis like this could have is huge. We are able to quantify customer responses with high accuracy, and use those scores to discover patterns that could benefit both marketing and operations. We already see how managers at all levels can leverage these findings to help improve operations, boost customer service ratings, and maintain brand reputation. We’re excited to share more on this topic in future blog posts where we’ll discuss the various Sentiment Analysis dictionaries, R-Studio with SQL Server, and performance indicators that can be leveraged in our business landscape. Stay tuned for more!
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