In an age where data is constantly being collected, it’s important to know how you can turn that data into actionable data and use it to improve your operations. We’re used to the term “data mining” but here, we’re really looking into “opinion mining” using sentiment analysis – and doing so through Power BI. When used and analyzed properly, sentiment analysis is a very powerful tool. It allows brands to understand consumer behavior and react based on those findings. So let’s focus on the benefits Power BI unleashes on users who are performing sentiment analyses.

1. You don’t need to be a data scientist

Using Power BI for sentiment analysis is really working smarter. Why? Microsoft’s Sentiment Analysis API-found in Microsoft Cognitive Services. The Power BI desktop allows users to better integrate Sentiment Analysis with a Power BI report than with an add-in (i.e. via Azure Machine Learning and Excel). With one API call through Microsoft Cognitive Services, you can get 1,000 scores (more than what you would get from using an add-in) indicating a positive, negative, or neutral score. The Text Analytics service offers natural language processing. When it’s given unstructured text, it can extract key phrases, analyze sentiment, and identify well-known entities (i.e. brands). These features will allow users to quickly know what customers are talking about and their feelings. Power BI can use data from many different sources such as a SQL database or Facebook. That being said, using the Key Phrases API does require users to include certain field data for each document including text, id, and language fields. The “out-of-the-box” Sentiment Analysis API allows users to nix the complex, intimidating algorithms.

2. Visualize the data

Once you have that data, it’s important to have the ability to relay that data to the team so you can act upon it appropriately. First, know that the Sentiment Score that is returned by Microsoft Cognitive Services Text Analytics uses a value between 0 (a negative sentiment) and 1 (a positive sentiment). Once the data is connected in Power BI, it can connect with other data tables and analyzed with many different types of visualizations. Power BI users can use these built-in visualizations and customize them. For example, pulse charts can allow you to build a timeline of events around reactions and reaction types. There is a lot of power in data visualization and it’s become a way to convey information to decision makers that makes vital information easier to understand. These reports can be interactive and in real-time- valuable for a Sentiment Analysis section connected to social media that is constantly gathering new insights.

3. Shaping and Streaming Data Sets

Again, Power BI makes it possible to not be a coding wizard. With just a few easy steps, users can create a Sentiment Analysis Solution by using Microsoft Flow and Power BI. You can create a number of different data sets from various sources using Power BI Desktop including Microsoft Excel, the web, text/CSV, SQL Server, and more. Today, your data could be coming from a lot of different data sources and Power BI doesn’t limit that connectivity. Also as a lot of businesses move their data to the cloud, that’s not a problem either. Power BI can perform a Sentiment Analysis with data that may be in the Cloud or on-premise. Once you determine where your data will be coming from, Power BI Desktop allows you to shape and combine that data and customize it into one useful query onto a personalized dashboard.

4. Data Storytelling

Sentiment Analysis and Power BI allows users to take advantage of the new data storytelling trend corporations are looking for. It humanizes data and as previously mentioned, visualizes the data so the critical data that is being gathered and analyzed, can be turned into actionable data. Many corporations need a way to inform and engage their team to improve their overall operations – and Sentiment Analysis using Power BI provides a valuable strategy and solution to meet those needs. Business intelligence platforms – like Power BI – are meeting the needs of organizations by merging core business workflows, processes, and embedded analytics. Actionable analytics is expediting the decision-making process for data-driven companies. These organizations that have a ton of data coming in need a reliable reporting strategy when monitoring for example, feedback and behavior on social media.

5. Data Flexibility

Power BI is very flexible with its dataflows. Power BI’s dataflows allows users to have the ability to construct and prepare the data without having to program the Extract-Transform-Load (ETL) system – talk about a time saver! Users don’t have to wait around for specialists to build and test the ETL pipeline, just define the dataflow, test it, and if it doesn’t work, try again! The whole development process of a dataflow allows users to collaborate with their whole team with ease. Power BI also has a social engagement content pack where users can analyze an organization’s engagement on social media with KPIs based on sentiment, location, authors, and tags.

In an age where opinion mining isn’t going to be slowing down by any means (the number of monthly active Instagram users alone has grown to 1 billion according to their latest reports). Building a Sentiment Analysis through Power BI will allow organizations to gain valuable insights and act upon them in order to maintain and improve overall operations.

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