The Importance Of Sentiment Analysis In Surveys
Survey sentiment analysis is used to identify the sentiment behind the opinions in survey responses and is most widely used in analyzing open-ended surveys. A company needs to analyze the voice of the customer by first gathering customers’ feedback about their experience with the brand’s products or services. However, trying to make sense of the collective insights from customers’ wants, expectations, perceptions, and preferences can be a difficult task to master. This is especially true if the data you’re gathering involves open-ended responses. A survey data sentiment analysis solution helps address this challenge easily by using machine learning.
Let’s look at sentiment analysis in more detail and explore how it can be a valuable tool for survey data analysis.
What Is Sentiment Analysis?
Sentiment analysis is an automated process that sorts consumer opinions into positive, negative, and neutral. Thanks to machine learning and natural language processing, sentiment analysis assigns weighted sentiment scores to the topics, themes, and categories within a sentence or phrase. A sentiment analysis solution is not limited to analyzing text data. With the help of video AI it offers several capabilities like video content analysis to search inside video, while helping you interpret media formats including images so you can gain a holistic view of customer insights. Survey sentiment analysis helps companies gauge public opinion, conduct market research, monitor brand and product reputation, and understand the customer experience (CX).
What are the Major Advantages of Using Sentiment Analysis for Customer Surveys?
Survey data sentiment analysis offers a more complete view of customer behavior and perceptions. By categorizing sentiments (i.e. positive, negative, neutral) and analyzing data from survey results, you can identify any weaknesses in your product or service. As a result, you get the information and insights to help improve yourself before it hits the sales cycle.
Below are the major benefits of survey analytics with sentiment analysis.
1. Open-Ended Responses
Using survey data sentiment analysis allows you to analyze semantics in open-ended responses. So, instead of relying on close-ended questions that provide limited insights, you can gain even more information through voice of the customer analysis of open-ended responses. That way, you can understand what customers like about your brand, what they don’t, and what you can do to enhance the CX.
Without survey sentiment analysis, it would be difficult to make sense of responses to open-ended responses. However, survey analysis software that uses AI and natural language processing (NLP) algorithms can solve this challenge. It can also predict survey response patterns as you conduct survey data analysis with semantics.
2. Identify Key Similarities In Semantics
In some cases, survey responses may be similar to each other. However, they could have completely different meanings. And this can cause you to inaccurately interpret survey results. For instance, if a survey participant uses an open-ended response with something like, “better selection of products”, it can be difficult to determine whether that response is positive or negative. That’s because the participant could either be asking you to improve your selection of products or they could be responding positively to a question - “why do you prefer this e-commerce website to another?”
With semantics and machine learning, you can differentiate between similar responses in surveys. That’s because there’s no reference point regarding open-ended responses.
3. Extract Sentiment For Each Aspect
A sentiment analysis API delivers useful insights through survey sentiment analysis by extracting emotions expressed for each aspect, service, or feature in survey data. For example, imagine that a customer says, “The food is amazing. But I’m not sure I’ll be going back since the wait is too long. Maybe have more waiters or waitresses available or consider hiring more cooks.”
With survey sentiment analysis, you can determine that the customer enjoyed the food, but the time it took for them to receive their meal might drive them away to another restaurant. Even though the respondent gave a compliment (the food is amazing) and made a complaint (the wait time was too long), they still provided a possible method of solving the problem (hiring more cooks and waiters/waitresses).
4. Identify Sentiment Changes Over Time
Another strong feature of semantics is that it allows you to measure and track changes in sentiment over time. The technology can also compare historical and current data. If you use a solution that offers a survey data sentiment analysis dashboard, you can map current customer feedback data and extract entities and aspects of emotion mining. And as time passes, you can see any changes in sentiment data.
This data can not only help you improve your product and CX, but it can also be useful for brand monitoring and reputation management. And as data begins moving towards a positive sentiment, you can identify what you’re doing right and how you should invest your marketing budget.
5. Identify Aspect Co-Occurrence
With survey data sentiment analysis, you can identify recurring topics and aspect co-occurrences, which allows you to gain deeper insights. Co-occurrence means that two aspects appear together more often than not when customers write a review. Therefore, it can give a lot more insights than just expressing the results positive and negative. Identifying aspect co-occurrence can also help to determine if some aspects co-occur more often than others.
An example of this would be customers responding with the terms “spacious” and “room” in the context of the hotel industry. Imagine that both aspects occur at a more frequent rate together than other aspects (i.e. “room” and “traffic”). Therefore, semantics in a survey analysis software can conclude that customers must be referring to the “comfort’ of their room.
6. Analyze All Types of Media Formats
Customer experience insights often come in the form of text. However, some customers respond to surveys with other media formats, including images, video, and audio. Using survey analysis software, you can analyze data in all formats and process them seamlessly, without having to leave out pertinent details. Also, some survey analysis platforms can read and interpret special characters. For example, if a customer uses an emoji in their response, the software can more easily identify the sentiment the customer expressed. So, if the customer’s response was ambiguous, survey sentiment analysis software can use special characters to determine whether a customer’s sentiment was positive or negative.
The Importance of Semantics in Survey Analysis
Survey data sentiment analysis software can help you gain insights into what customers want in your brand and products. Survey sentiment analysis can also provide accurate information on where there might be weaknesses in your products or services. In addition, survey data analysis can show you where you can improve your offering by capturing consumer insights.
Survey data sentiment analysis software can analyze the following types of survey data:
- Social media likes
- Customer satisfaction scores
- Answers to close-ended questions
However, the survey data above are examples of quantitative data. Customers also provide insights in the form of responses to open-ended questions. Therefore, qualitative data types can be difficult to interpret and measure. But with survey sentiment analysis software, natural language processing can identify similarities in semantics even if survey data is unstructured like it is with open-ended responses.
For example, imagine that a customer responds to a survey with, “Your customer service is a laugh. I’ve been on hold for over 30 minutes!” If you’re not using survey data sentiment analysis software, it can be easy to misinterpret the word “laugh” as positive. But on the other hand, semantics in survey sentiment analysis software helps you get a better understanding of the context of naturally spoken language and extracts information from unstructured data with human-level accuracy.
Transform Your Survey Analysis Process With Repustate
As you gather more and more insights about your customers, making sense of significant amounts of data is paramount. This is why using survey analytics with sentiment analysis can be a significant step in the right direction. As a result, you can sieve the relevant data as well as synthesize and extract useful information from it. This can empower you to improve your products or services to a level your customers expect of you.
Repustate’s sentiment analysis solution for survey data can help you more effectively and efficiently analyze and interpret customer surveys.