Sentiment Analysis in Excel with Azure Machine Learning

Introduction:
Understanding customer sentiment is essential for organizations in today’s data-driven world. Sentiment analysis, a method for identifying the emotional undertone of text data, can offer insightful information about customer sentiments and aid in decision-making. In this blog post, we’ll look at how to use a tool that could surprise you to perform sentiment analysis: Microsoft Excel.

Step 1: Gathering and preparing data
We need data to evaluate before we begin sentiment analysis. Start by gathering text data, such as customer reviews, comments on social media, or poll results. Let’s assume, for the purposes of our example, that we have a dataset with customer reviews of a hotel stay experience.

Step 2: Accessing Azure Machine Learning Add-in

Now that we have our data prepared, we’ll seamlessly integrate the power of Azure Machine Learning into our Excel environment. Follow these simple steps:

• Click on the “Insert” Tab: In your Excel application, navigate to the “Insert” tab located at the top menu bar.
• Choose “Get Add-ins”: Within the “Insert” tab, you’ll find an option labeled “Get Add-ins.” Click on it to open the Add-ins pane.
• Search for Azure Machine Learning: In the Add-ins pane, you’ll see a search bar. Type “Azure Machine Learning” into the search bar and hit Enter.

Step 3: Seamless Excel Integration

The Azure Machine Learning add-in smoothly interacts with your Excel environment once you’ve added it. Here is what follows:

Excel will instantly redirect you back to your regular Excel workspace when you add the add-in.

Due to the incorporation of Azure Machine Learning, Excel now offers new options or functions. You can use these capabilities and tools to access the power of sophisticated analytics and machine learning directly from an Excel application.

We’ll look at using these integrated tools to perform sentiment analysis on your data in the following steps, making data-driven insights more available than before.

Step 4: Data Column Name Adjustment

It’s important to check that our data is properly formatted before we go into the interesting area of sentiment analysis. It’s vital to check that our data is properly formatted before we go into the interesting area of sentiment analysis. Future errors may be avoided with this small modification.

• Click on “View Schema”: To ensure a smooth analysis process, click on the “View Schema” option in your Azure Machine Learning add-in within Excel.
• Identify “Input 1 Tweet_string”: In the schema view, you’ll see a column labeled “Input 1 Tweet_string.” This column represents the text data that we’ll analyze for sentiment.
• Change the Column Name: To match the expected column name, make sure to rename this column as specified in the “View Schema” instructions. Ensuring that your data column name aligns with the expected format will help avoid any potential errors during the sentiment analysis process.

By making this simple adjustment, you’re taking a proactive step to ensure a seamless sentiment analysis experience. With your data correctly structured, you’re now ready to harness the power of Azure Machine Learning to derive valuable insights from your text data. Let’s move forward with our sentiment analysis journey!

Step 5: Configuring Input and Output Ranges for Sentiment Analysis

Now that we’ve ensured our data is correctly structured, it’s time to configure both the input and output ranges for our sentiment analysis. Follow these straightforward steps:

• Click on “Predict”: Within your Azure Machine Learning add-in in Excel, locate and click on the “Predict” option. This is where we’ll configure the input and output ranges for sentiment analysis.
• Specify Input Range: In the “Predict” dialog box that appears, you’ve already specified the input range eg :A1:A12. This is the column in your Excel spreadsheet where you’ve stored your reviews or comments data.
• Specify Output Range: In the same “Predict” dialog box, you’ll also need to specify the output range eg:B1. This is where the sentiment analysis results will be placed in your Excel sheet. Choose a column or range of cells where you’d like to see the sentiment analysis scores or labels.
• Review and Confirm: Take a moment to review your configuration. Ensure that you’ve accurately specified both the input and output ranges, and everything looks as expected.
• Proceed with Prediction: Once you’re satisfied with your configuration, click the “Proceed with Prediction”.

Step 6: Extracting Sentiment and Scores

With our input and output ranges configured, it’s time to extract sentiment and scores based on the reviews.

Step 7: Visualizing Sentiment Analysis Results

Now that we’ve successfully extracted sentiment and scores from our comments, it’s time to visualize the results in a chart to gain a deeper understanding. Visualization can be a powerful way to convey insights:

Conclusion

In conclusion, our journey through sentiment analysis in Excel with Azure Machine Learning has empowered us with a robust toolset for understanding customer sentiments. By seamlessly integrating these two powerful platforms, we’ve unlocked the ability to make data-driven decisions, enhance products and services, and respond effectively to customer feedback.