Python in Excel - Build an Automatic Sentiment Analysis and Classification Function (IN 10 MINUTES!)

⏱️ Timestamps:

0:00 – What is sentiment analysis?
0:25 – How to interpret and Analyze Sentiment in Python
0:35 – Compound Score explanation
0:50 – Reports we are going to build (Positive, Negative, Neutral Classification Report & Count of Positive, Negative, Neutral)
1:10 – How to import the Sentiment Intensity Analyzer function for use in Python in Excel
1:40 – Example output of sentiment analysis results using the SentimentIntensityAnalyzer().polarity_scores() function
2:24 – How to interpret sentiment analyzer function’s results
3:15 – How to convert dictionary results of sentiment analyzer function to a pandas DataFrame
4:00 – ⚠️ Warning about dragging down Python formulas and Python in Excel usage quotas
4:57 – How to build a basic for loop to process and record results of sentiment analysis from multiple statements using one Python cell 🔁
6:07 – How to convert list of dictionaries into a pandas DataFrame
6:58 – How to classify sentiment compound ratings as ‘Positive’, ‘Negative’ or ‘Neutral’ with an if block
8:00 – How to add a list as a new column in an existing pandas DataFrame
8:30 – How to spill Python pandas DataFrame as Excel values (so DataFrame shows up in Excel cells)
8:43 – Reorganizing and removing columns from an existing pandas DataFrame
9:31 – Example output for the custom function we built that automatically classifies the sentiment of the statements that we select
9:53 – Generate sentiment report summary custom function using .value_counts pandas DataFrame function
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If you are brand new to Python, check out some of my other videos to get a grasp of Python in Excel fundamentals. My video on basic for loops and if blocks might be particularly helpful.
▶️ YouTube Homepage:    / @learnpythoninexcel  
📹 Basic For Loop and If Block Video:    • Python in Excel Tutorial - Intro to Basic ...  
📄 Demo File I use in the video: https://www.spreadsheetwranglers.com/...
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📌 About this video and next steps:

In this tutorial, I’ll show you how to use Python in Excel to categorize short text—tweets, product reviews, or even financial texts—into positive, neutral, or negative sentiments. If you’re wondering how to analyze text, you’ll see how easy it can be to combine Excel programming Python with the power of VADER from NLTK to classify your data directly in a spreadsheet. This core technique helps you conduct sentiment analysis on a variety of opinionated statement formats including tweets, Amazon reviews, and more.

We’ll focus on the basics: importing the NLTK library and using Python DataFrames in Excel, and coding a quick script to give each review a sentiment label. By harnessing Excel Python capabilities, you can quickly expand into diverse applications—such as review classification or sentiment projects involving broader statements that you need insights on.

If you’ve ever wanted to see a real-world example of how to conduct sentiment analysis or explore sentiment analysis in NLP, this is a practical place to start. You can adapt these methods for tweets sentiment analysis, sentiment analysis on Amazon product reviews, or more specialized tasks like trading sentiment analysis.

To keep it simple, we use thresholds to label the text as negative, neutral, or positive, then display aggregated statistics. For advanced workflows—beyond the scope of this video—many experts consider exploring additional sentiment analysis benefits like real-time monitoring or domain-specific dictionaries. Tools such as Analytics Vidhya data science resources can guide you further in scaling up your natural language processing projects.

If you’re interested in deeper customization or cross-referencing multiple techniques, you might look into sentiment lexicons or frameworks like Python transformers, vector similarity search, and Hugging Face pipeline to refine more complex classifications using Anaconda in Excel. Similarly, terms like FinBERT model can be relevant if you decide to pursue forex sentiment analysis or highly specialized sentiment analysis in natural language processing for financial data. Although we won’t explore them here, keep these keywords in mind as you grow your text analytics skills.

Throughout this sentiment analysis tutorial, you’ll see that Python in Excel (particularly Python in Excel 365) opens the door to straightforward yet powerful text classification. Even if you’re new to how to code Python in Excel, you can follow along and experiment with small datasets, then scale up to handle bigger tasks like Twitter sentiment analysis NLP. By combining Python inside Excel with standard spreadsheets, you’ll discover a beginner-friendly way to incorporate sentiment analysis using natural language processing into everyday workflows. So, dive in, learn the essentials, and don’t forget to subscribe for more tips on Excel Python tutorial topics and beyond! 🔍📊💬