Excel Sentiment Analysis: Sentiment Analysis - Episode 2062

Microsoft Excel Tutorial: Sentiment Analysis in Excel.

Welcome to another episode of the MrExcel Podcast, where we explore all things Excel. In this episode, we will be discussing sentiment analysis in Excel. It all started on Thanksgiving night when a friend of ours mentioned wanting to do sentiment analysis on Twitter data. I immediately realized that Excel has a way to do this and decided to create a video on the topic.

But first, what exactly is sentiment analysis? Well, if you have a survey with multiple choice options, it's easy to analyze the data. However, when you have long answers with sentences or paragraphs, it becomes difficult to manually go through and determine the sentiment. This is where sentiment analysis comes in. There are two types - human supervised learning and machine learning. In the past, human supervised learning involved going through a portion of the data and categorizing positive and negative words. However, this method is limiting and requires constant re-categorization for different data sets.

This is where Excel's Azure Machine Learning comes in. It uses the MPQA Subjectivity Lexicon, a generic dictionary with over 5,000 negative words and 2,500 positive words. This method works well for short sentences, Tweets, and Facebook posts. However, it may struggle with double negatives. To use this tool, simply go to the Insert tab, click on Store, and search for Azure Machine Learning. Then, specify your input and output range, making sure to change the heading to match the Schema.

Once the analysis is complete, you will have two columns - Sentiment and Score. The Score represents the percentage of positivity or negativity, with 100% being extremely positive and 0% being extremely negative. You can use this data to create a pivot table and see the average score for each sentiment category. This is a quick and efficient way to analyze large amounts of data and determine the overall sentiment. So next time you have a lot of data to analyze, give sentiment analysis in Excel a try using the free Azure Machine Learning Add-in. Thanks for watching and be sure to tune in for more Excel tips and tricks from MrExcel.

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Table of Contents:
(00:00) Introduction to Sentiment Analysis in Excel
(00:29) Using Excel for sentiment analysis
(01:11) Limitations of human supervised learning
(02:08) Introduction to Azure Machine Learning
(03:06) Steps for using the Text Sentiment Analysis Excel Add-in
(03:43) Results and interpretation
(04:47) Using a pivot table for further analysis
(05:27) Recap
(06:15) Clicking Like really helps the algorithm

It is easy to quantify survey data when it is multiple choice
You can use a pivot table to figure out what percentage each answer has
But what about free-form text answers? These are hard to process if you have hundreds or thousands of them.
Sentiment Analysis is a machine-based method for predicting if an answer is positive or negative.
Microsoft offers a tool that does Sentiment Analysis in Excel - Azure Machine Learning.
Traditional sentiment analysis requires a human to analyze and categorize 5% of the statements.
Traditional sentiment analysis is not flexible - you will rebuild the dictionary for each industry.
Excel uses MPQA Subjectivity Lexicon (read about that at http://bit. ly/1SRNevt)
This generic dictionary includes 5,097 negative and 2,533 positive words
Each word is assigned a strong or weak polarity
This works great for short sentences, such as Tweets or Facebook posts
It can get fooled by double-negatives
To install, go to Insert, Excel Store, search for Azure Machine Learning
Specify an input range and two blank columns for the output range.
The heading for the input range has to match the schema: tweet_text
Companion article at: http://sfmagazine.com/post-entry/may-...

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