How to Run Sentiment Analysis in Excel with Copilot (Step-by-Step Tutorial)

Quick Overview

Microsoft Excel's Copilot feature can analyze customer feedback by identifying sentiment and categorizing themes, transforming raw data into actionable insights through simple prompts and even creating visual aids like pivot charts.

Key Points: Copilot can be used within Excel to perform sentiment analysis on customer feedback by identifying positive, negative, or neutral tones. The 'Feedback Theme / category' prompt allows Copilot to categorize feedback into specific topics like 'Product Quality', 'Packaging', or 'Customer Service'. By using a pivot table, users can count the occurrences of each sentiment or feedback theme, providing a quantitative overview of customer opinions. Excel's pivot charts can then visualize this data, offering a clear, graphical representation of feedback themes and their prevalence. The process involves using the function with specific prompts, such as 'give me the sentiment as emoji' or 'give me the feedback theme / category', referencing the data range. The tutorial demonstrates how to extract specific insights, like filtering feedback related to 'pricing', and then summarizing it effectively. The capabilities extend to generating word clouds and more advanced analysis, making data interpretation more efficient.

Context: This tutorial showcases how to leverage Microsoft Excel's advanced features, specifically the integrated Copilot AI assistant, to analyze customer feedback. It demonstrates practical applications of sentiment analysis and thematic categorization directly within spreadsheet data, transforming unstructured text into organized, quantifiable insights.

Detailed Analysis

This tutorial demonstrates how to use Microsoft Excel's Copilot feature to perform sentiment analysis and categorize customer feedback. The process begins by selecting the relevant data range containing customer comments. Copilot can then be prompted to identify the sentiment of each piece of feedback, outputting results such as 'Positive', 'Negative', or 'Neutral' directly into a designated 'Sentiment' column using the formula. The AI can also categorize feedback into themes, like 'Product Quality', 'Packaging', or 'Customer Service', by using prompts like . For more advanced analysis, users can then create pivot tables from this categorized data to count the frequency of each sentiment or theme. Further visualization is achieved by generating pivot charts, such as a pie chart, to represent the distribution of feedback themes graphically. The tutorial highlights the efficiency and ease with which these AI-powered tools can extract meaningful insights from large datasets, enabling users to quickly understand customer opinions and identify areas for improvement.