Sentiment Analysis of Survey Responses

Sentiment Analysis of Survey Responses

Your customer surveys contain both negative and positive responses, and you probably want to handle the negative responses first, given that they often contain more urgent issues.

Analyzing open-ended responses in customer surveys, and creating a report that will provide valuable insights, is easier said than done. It may take hours, days or even weeks to go through survey responses if you’re analyzing them manually.

So how can you speed up this process, and quickly detect problems that are leading to negative responses?

Sentiment analysis can help you automatically sort your survey responses in next to no time, so you can answer questions like:

  • How many negative responses did we receive?
  • What aspects of our product do customers love?
  • What aspects of our product do customers hate?
  • Has a particular product feature improved?

In this guide, learn how to perform sentiment analysis on your survey responses.

What Is Sentiment Analysis?

Sentiment analysis is the automated process of sorting opinions into positive and negative. Equipped with machine learning and natural language processing, a sentiment analysis model can understand human-generated text data in survey responses and tag them as positive, negative or neutral.

Sentiment analysis can also be combined with aspect classification to create an aspect-based sentiment analysis model. An aspect classifier can pinpoint different topics or themes that are mentioned in a survey response, for example, Price, UX, and Customer Support.

So, to learn how customers feel about a specific topic or product, you can combine a sentiment classifier and an aspect classifier.

Why is it Important to Analyze Sentiment in Surveys?

Analyzing qualitative data in survey responses can seem like a never-ending task if done manually. It’s tedious work, and agent time could be better spent on more important tasks.

That’s why it’s essential to automate this repetitive task using sentiment analysis tools. You can reduce the number of hours spent on survey analysis to just a few minutes.

Some of the main benefits of using sentiment analysis for processing survey responses are:

Get more accurate and better customer insights:

We often get tired, disagree with each other, and interpret things differently. So it’s not easy for a person, let alone an entire team, to tag survey responses consistently without any errors. A sentiment analysis model doesn’t hesitate when tagging – it tags all your data using the same criteria, so you can be confident that your insights are accurate.

Sentiment analysis, combined with topic analysis, can also help you pinpoint exactly where changes need to be made, so you’re never left second-guessing.

Analyze your customer surveys in real-time:

Are customers upset with a price change you recently made? Did yesterday’s bug affect customer experience? You know that tackling problems and spotting opportunities is hard when you’re out of step with customer feedback. With sentiment analysis, you can analyze your customer survey data as soon as it appears in your help desk. This way, you can take immediate action on any urgent issues.

Scale easily as your data grows:

Thousands of survey responses are processed every week and it’s unrealistic for teams to deal with them manually. Long hours of manual work could be redirected to more human-dependent tasks. By implementing sentiment analysis into your processes, you can let machines do this repetitive work for you. AI sentiment analysis tools like MonkeyLearn let you scale up or down as needed, so you only pay for the data you analyze.

Tackle problems before they grow:

Sentiment analysis can help you improve customer experience by detecting negative survey responses right away. Besides ensuring that customers feel like you’re listening to them, you can tackle problems before they turn into something bigger. By taking action sooner than later, you can even turn a bad experience into an exceptional one.

How to do Sentiment Analysis of Survey Responses

Sentiment analysis is no longer exclusive to developers or data scientists thanks to the advent of user-friendly and easy-to-use machine learning tools. Once you learn how to use them, you’ll stop wasting valuable time on tedious work. But first, let’s see how you can apply it to your day-to-day work.

Gather Survey Responses

The first thing you’ll need is data (survey responses)to train your sentiment analysis model

There are different ways to gather your survey responses depending on what survey tool you use. We’ll cover the most popular ones below:


SurveyMonkey is one of the most popular tools for creating online surveys. It offers the possibility of exporting survey responses to a CSV or XLS file for paying clients. Just go to the ‘Analyze Results’ section of the survey, click on ‘Save As’ and ‘Export file’. There you will have to choose between ‘All Summary Data’ (to organize the survey by question) or ‘All Responses Data’ (to organize the survey by respondent). Don’t forget to select ‘Include Open-ended Responses’, since this is what you’ll be analyzing with a sentiment analysis model:


Another popular tool for creating online surveys is Typeform. Unlike SurveyMonkey, Typeform allows all users (free and paid) to download responses in a CSV or XLS file. In your Typeform survey, click on ‘Download all responses’:

Next, Typeform will ask you to choose the format you want to use to download the survey responses. Choose the option you want and then click the ‘Download’ button:

And voilà! You’ll have your survey data on your computer.

Google Forms

You can easily open up your Google Forms on a Google Sheet by going to the ‘Responses’ tab and then clicking on the Google Sheet icon:

Now, download the responses as a CSV or Excel file by going to ‘File’ > ‘Download as’:

And you are good to go.

How to Analyze The Sentiment of Your Survey Responses

Now that you have your survey responses in a spreadsheet, the next step is to analyze them for sentiment.

With MonkeyLearn's Templates, you can upload your spreadsheet data and our sentiment machine learning models will do the rest. Templates are super simple to use and perfect for anyone who’s new to machine learning.

Here’s how MonkeyLearn’s NPS Survey Template works:

1. Choose the NPS Template

Choose the NPS template to create your aspect-based sentiment analysis workflow. This template also combines topic analysis and keyword extraction to get even more granular insights.

2. Upload Your Data

If you don't have a CSV file:

3. Match your data to the right fields in each column

Here are the field you’ll need to match up:

  • created_at: Date that the response was sent.
  • text: Text of the response.
  • score: NPS score given by the customer.

4. Name Your Workflow

5. Wait for MonkeyLearn to process your data

6. Explore your sentiment analysis dashboard!

Our data visualization dashboard allows you to display your results in convincing, striking detail. 

  • Filter by sentiment, topic, keyword, score, or NPS category.
  • Share via email with other coworkers.
  • Notice how multiple aspects and sentiments have been split into fragments, otherwise known as opinion units.

Machine models that have been trained to detect opinion units are much more precise when it comes to analyzing data. Why? Well, it’s a lot easier for a machine to understand a sentence with one sentiment, than it is to understand a sentence containing multiple sentiments.


Open-ended survey responses provide valuable insights that can help you understand your customers’ needs and frustrations, and sentiment analysis can help you prioritize accordingly to provide the best possible customer experience.

Within a few hours, you can share valuable insights with your team and start making decisions that give yourbusiness a competitive advantage.

Interested in learning about how you can use sentiment analysis on survey responses? Request a demo from one of our experts.

Federico Pascual

April 25th, 2019

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