The creation of data, globally, has increased unexpectedly. With approximately 170 MBs of data being producing by every person every other second and over 2.5 quintillion bytes of data created every day. About 90% of the world’s data is generating in the last two years. Moreover, With the digitization of more data due to the current pandemic’s influence and the growth of the internet, and the ubiquity of social media. The trajectory of data generation is going to increase further.

Currently, the need for efficient data management by businesses is a great one. First-generation machines were available to standardize the way data was recording. We then moved to data mining for handling large volumes of data. Now, we have transitioned towards machine learning for better representation, visualization, and correlation of data.

Text Analytics Using Power BI

However, there are many software services and platforms that assist with Business Intelligence. Incorporating enhanced tools and services to cater to the need for data management. Businesses are now able to produce interactive reports and coherent data visuals. For instance, one such analytics service is Microsoft Power BI Text Analytics.

Power BI Text Analytics

Text analytics in power BI intends to let businesses create better insights to be well informed of their current standing and analyze the data to make better decisions. Whether data is available in data warehouses or the form of spreadsheets and documents, Text Analytics using Power bI provides a user-friendly platform to connect all your data logically.


Power BI offers some key features that users can take advantage of. These features include:
  • Language-based Q&A, where users can ask questions to navigate their data without the use of any code or syntax.
  • Quick Insights, which allows end-users to draw correlations, trends, and comparisons of data sets in a matter of seconds.
  • Usage of M-Functions, a functional programming language that allows developers to apply transformations across various data sources and save these M expressions as queries.
  • Source Queries for Direct Query Models. They allow users to create instant visibility of business processes, with real-time datasets, through the latest SQL Server Analysis Services (SSAS).
  • Various filtering capabilities across many components such as queries and data models.
  • Integration of many queries with append functions. This is done with the incorporation of files from different network locations and sources.
  • A Data Modeling Feature and Data Analysis Expressions (DAX). It allows handling of Many-to-Many Relationships.
  • Integration of Math and Statistical Analysis for improved forecasting of historical data.
  • Ensuring user-friendly experience with integration of IOS, Android, and Windows devices for reports to be available.

Likewise, a plethora of other features is available for businesses. Accommodating several advanced and basic extrapolations of data. Yet, manipulation of data through analytics is not limited to financial modeling and analyzing customer relationships through sales. Critical business decisions are made through text analytics as well.

User Satisfaction:

Microsoft Power BI Text Analytics or Text Mining is another innovative feature for understanding the experience that is available for customers through products and services. Their reviews on official websites, social media, and surveys are there to gather insights to make penultimate operational decisions. Additionally, Text Analysis Power bI simplifies this accumulation process and minimizes the time for drawing analysis to a matter of seconds. The need to achieve customer satisfaction is pivotal to any organization and Power BI aids in the incorporation of user feedback to dwell deeper in the industry and gain a competitive edge over competitors.

On the other hand, a feature that makes text analysis easier is Sentiment Analysis. It can check all the reviews made by users, across different platforms, and interpret large quantities of text-based data. Also, It allows businesses to import these comments and perform keyword analysis to identify the words that either elicit a response or a sentiment. Each review is given a polarity score that can rank for positivity or negativity.

Enterprises have a choice between the type of analysis they want to run for their Power BI text analysis needs. They can either perform:

1. Emotion-based Sentiment Analysis

There is a natural emotion triggered by the use of a product or a service. The user can be an express either delight if a problem resolves, or frustration if they do not get what they expected. Similarly, any kind of sentiment that highlights happiness, sadness, anger, and disappointment can be scored by the Emotion-based Sentiment Analysis. This can help come up with advertisements and marketing campaigns.

2. Aspect-based Sentiment Analysis

The Aspect-based Sentiment Analysis is useful when coming up with keywords that explain how the customers feel about a product or service. yet, being allowed to analyze common topics of discussions in reviews, the Aspect-based Sentiment Analysis allows businesses to better understand why a certain emotion appears.
Power BI Text Mining has other extra tools to assist in the visualization of this sentiment-based analysis. Visualize sentiment analysis in power bi can represent keywords and phrases in a Word Cloud format to isolate the most used words and emotions. Additionally, Slicers are another tool that can later available for managers and teams to study the filtered words from the word cloud.
Analysis of any kind is incomplete without the use of charts and graphs to check correlations between datasets and business operations. Similarly, another Data visualization tool available in Power BI is to perform a quantitative analysis of the assigned sentiment scores. The available representations are:
  • Line Charts to identify the trending sentiment scores over different quarters.
  • Column charts for manages to check different sentiment scores with their respective teams.
  • Bar charts for analyzing the cross-functional performance of teams.
  • Histograms represent the distribution of numerical data that can make executive decisions.
  • Box and Whiskers Plot to understand the variability of sentiment scores outside the upper and lower quartiles.


Therefore, with the use of these tools Power BI helps create an interactive environment among organizations to study existing trends and forecast future goals and targets. Also, these tools help extrapolate the business value and health of the organization. They are based on the sentiments expressed by users for their products and services. Moreover, with the advent of social media and digital platforms that allow users to share their experiences, Power BI aims to play an imperative part in evaluating these large volumes of data to drive future sales and determine the necessary actions for the growth and development of the company.