How to Analyze Survey Data with Emotion Analysis

Making your business a success is all about meeting customer needs. It’s as simple as this – would you give someone a knife when they want a spoon? Hence, you need to know what your customers want from you. Surveys are a great tool to understand their requirements and collect feedback on your brand experience. From designing a new product line to logistics and brand image, surveys play an important role in uncovering consumer insights. 

Setting up your survey questions

Surveys should be designed to collect maximum information with minimal effort by the consumer. They typically contain close-ended and open-ended questions. Close-ended questions like multiple choice, ranking, checkbox or drop-down questions have anticipated results that are easy to quantify. 

Let’s say you’re introducing new colors to a product and ask customers to pick between red and black. The results can be easily analyzed and you get a clear picture of your customer’s choices from the options presented to them.

But, what if you simply ask respondents to type their preferences? The results are not limited to 2 choices, you could also get variables like pink, green, blue, yellow, and so on as options. This is an example of an open-ended question. 

Responses to open-ended questions may be a word, a phrase or even a paragraph. This type of data is categorized as qualitative data. Qualitative data analysis involves identifying themes, interpreting patterns and determining how these themes and patterns answer your questions. 

With sentiment analysis, you can further transform qualitative results into quantitative results. For example, you could learn that 60% of your survey respondents prefer red while 20% prefer black and 10% prefer green. Let’s dive deeper into emotional sentiment analysis and survey data. 

The importance of open-ended survey questions and qualitative data

Before delving into sentiment analysis for qualitative questions, it’s important to first assess the need for them. Having a survey with only quantitative results would definitely be easier to analyze and interpret. Give respondents the option to rank their preferences for products. 

Create a choice between ‘Yes, I like it’ and ‘no, I don’t like it’. Let them rate their level of satisfaction on a scale of 1 to 10. The responses are statistical and can be easily analyzed to tell you what customers like and what they don’t like. 

However, you might miss out on important details. A simple ranking system would tell you if customers like or dislike your products but it won’t tell you ‘why’ they feel this way. It is only by including questions that require qualitative responses and analyzing the sentiment behind these responses that you can get the answer to ‘why’. 

You might also accidentally omit an option and hence get a skewed response. Take this survey question for example;

“How did you hear about us?

• Facebook 
• Instagram
• YouTube
• Twitter”

By limiting the variables to make this a closed-ended question, other communication channels like word of mouth, print and television advertisements, etc. have been omitted. This can be easily rectified by making it an open-ended question. 

Open-ended questions also put you in a position to learn more from a small sample of respondents. For example, let’s say you introduced a new product and want to survey beta users on how they feel about it. Along with close-ended questions that tell you whether they like it or don’t, including open-ended questions can give them a way to tell you what specific features they think need improvement. 

What is emotion analysis?

Emotion analysis or sentiment analysis is all about understanding the emotions behind a customer’s answers to a survey by using Artificial Intelligence (AI) types of software that do text analytics, Machine Learning (ML) and Natural Language Processing (NLP).  

At its core, emotion analysis categorizes answers as positive, neutral and negative. It also analyzes the themes and subjects respondents are writing about. For each subject, it scores a response from –1 to 0 in the case of negative responses and numbers, 0 to 1, for positive responses. Neutral responses are scored as the number 0. These statistical responses make the interpreted results easier to visualize. 

Sentiment analysis for surveys can help answer questions like:

• Which product has the most positive feedback?
• What do customers like and dislike about a product?
• Is the feedback trending in an overall positive or negative direction?
• Has there been a shift in the nature of responses since last year or last interval?

The variable of potential answers to these questions are vast. Learn more about sentiment analysis in our detailed article here.

Why is emotion analysis important for survey analysis?

Manually reading through every survey response, cross analyzing, and understanding the sentiment behind it is next to impossible. It’s tedious and there are a million better ways you can spend your time. Hence, it is best to automate the task with an emotion analysis tool. Let’s look at some of the benefits of doing so. 

Accurate customer insights

Analyzing statements to understand the emotion behind them manually can quickly become subjective. Two people can look at the same statement and score it differently based on their own experiences. Hence, it isn’t easy to be consistent with manual emotion assessment. However, by automating the task, the same criteria are used to evaluate each statement. Hence, there are no biases. 

Secondly, if you were to complete the task manually, you would probably pick a small sample from the survey answers to base your analysis. But, automating the task allows you to consider all the survey responses. As a result, the customer insights gained are more accurate and reliable. 

Real-time survey analysis

A survey can have as little as 1 question. For example, you may want to include a survey at the end of every order confirmation to find out how the customer rated the experience and what they would like improved. 

Using a sentiment analysis AI tool allows you to assess each survey response in real time. This helps you keep pace with customer expectations and spot issues and opportunities as they arise. 

For example, if you see a spike in disgruntled clients, you know there is something wrong and can look into the sentiment expressed by them to understand where the problem lies. 

Ability to tackle issues quicker

In continuation of the above point, emotion analysis helps you detect negative emotions at an early stage and give you the information required to tackle the issue before it grows bigger. For example, if you notice more than a few survey answers talking negatively about the quality of stitching, you can take a quick look at the product for yourself and make changes if required before you start losing customers. 

Step-by-Step Approach to Emotion Analysis of Survey Data

Today, conducting a survey is easy. You can include survey questions as a final step in the order placement process, send surveys by email, add surveys to landing pages, etc. The first step in emotion analysis of a survey is to collect all the responses in one place. Once you have all the information required, here’s what happens. 

Thematic analysis

Thematic analysis is based on deducing the meaning of the words used by people in submitting their answers. This can be done by identifying repetitive types of themes and patterns that emerge by grouping words and themes. The feedback is then quantified according to the sentiment expressed. Thus, there are 3 main steps involved:

Identify what respondents are talking about

The first step of analyzing qualitative responses in a survey is to identify the themes being talked about. For example, if you send out a survey to find out how clients feel about a product range, they may talk about price points, colors, material quality, availability, delivery timelines, etc. This will help you identify what is important to your clients. 

Since you can’t manually read through thousands of survey forms, you will need to train the AI model to recognize a list of qualifier words. For example, a theme like price points may be identified by keywords like expensive, value for money, cheap, budget-friendly, etc. Responses can then be categorized into different groups based on this analysis. 

The efficacy and accuracy with which AI software tools can categorize feedback depends on how many keywords are included in the list. Most text-processing platforms maintain a list of keywords and synonyms. In addition to a database of 750,000 words, Lettria allows you to modify the knowledge base to suit your needs. You can add product or industry-specific keywords as well as technical elements. 

For example, you can add slang terms like ‘spendy’ or phrases like ‘costs an arm and a leg’ to your database. The more keywords you include in your database, the less likely you are to miss out on responses. 

Analyze how much they are talking about each theme

There are numerous themes that may be identified in a survey. You need to identify which themes are being talked about the most to prioritize actions. For example, if people are talking about pricing as well as the quality of material, which aspect do you address first? Of course, you will want to address the point that is being talked about the most. For this, you need to know how many people are talking about each theme. 

Let’s say you find 200 people talking about delivery timelines, 350 talking about prices and 150 talking about the material. Thus, it is safe to say that prices are the most important criterion for the survey takers. This is what you need to tackle first. 

Identify how they feel about each theme

Now that the responses have been grouped into different categories, you need to identify the underlying sentiment correlation with each theme. This step is akin to the first step but with a smaller group of responses. 

Let’s take price as a theme. In this case, broadly speaking, you need to understand whether customers think the product is expensive, cheap or value for money.  In this instance, expensive may be considered a negative emotion while cheap and value for money are considered positive. Platforms like Lettria go beyond the three main emotions to include more complex emotions like anger, curiosity, disapproval, fear, pride, sadness, etc. 

To understand how customers feel about a theme, you must first categorize answers into various emotion groups. Simple statements, like “The product was perfect for my budget” or “too expensive for me” are easy to analyze and quantify. The first falls into the positive category while the second would be categorized as negative. 

However, not all feedback is as clear-cut. Let’s say a customer says, “It’s expensive but worth it.” The word expensive would give the statement a negative emotion score but in the overall context, this would be wrong. This brings us to the next step.

Put emotional responses in context

To analyze the emotions behind complex statements, the AI model must be able to understand the context in which the sentiment is being expressed. This is one of the advantages experienced by Lettria users. Lettria not only determines a statement’s polarity but also the emotional context within it. 

In the above example, “It’s expensive but worth it”, Lettria would assess expensive as a keyword but still give the statement a positive score. Similarly, some responses may be sarcastic or include slang terms. Your AI model’s ability to recognize them as such and handle textual data accordingly will determine its efficacy. Getting a concise report of all this data is essential.

Start analyzing your survey data today

Surveys are undoubtedly important for businesses in all industries. By implementing emotion analysis for your survey feedback, you can process all the textual data received and get a more comprehensive, accurate view of your customers needs. 

Emotion analysis platforms like Lettria’s software help quantify the qualitative responses and present them in a form that is easy to understand and derive value from. Though the NLP model is advanced with an expansive dataset, you can add keywords and synonyms even if you don’t have any technical knowledge. 

By allowing technical and non-technical profiles to contribute and collaborate on emotion analysis projects, Lettria gives you more meaningful results through a report that can be immediately put to use. Remember, the more you listen to your customers, the better you will be able to serve them.