Why Do We Need Semantics in Survey Analysis Software?
Survey analysis software helps you gain insights into what your customers want by analyzing your surveys using natural language processing and semantics. Survey data analysis can accurately tell you where there might be shortcomings in your products or services, and where you’ll be able to improve, by capturing consumer emotions hidden in open-ended questions with no predetermined answers.
AI-based machine models give you all the tools you need to effectively analyze survey responses, which will in turn allow you to introduce new products, improve current products, enhance your customer services, or give your marketing efforts a boost.
So let’s look in depth at the role semantics plays in survey analysis software and what features you should consider when choosing the best survey data analysis platform.
What Are the Types of Survey Data?
When analyzing survey data, you’ll get to deal with two different types of survey data, quantitative and qualitative. Quantitative data provides measurable data and is typically expressed in numbers like ratings, social media likes, customer satisfaction scores, but also includes the answers to close-ended questions.
Conversely, qualitative data provides data that’s often unstructured and expressed in words and, therefore, harder to measure. Data like this can be found in surveys using open-ended questions and would qualify as qualitative data.
What Is the Importance of Semantics in Survey Analysis Software?
Natural language processing tasks can identify semantic similarity in data even if they are verbose answers to open-ended questions that have no predetermined answers. Added to this, semantics helps keep a tab on changing sentiment over time, they can work equally well on data that comprises video, text, image, or audio formats. Semantics in survey data analysis also helps extract sentiment for each aspect or feature from the data that is being analyzed. Let’s delve into these a bit more.
1. Semantics in open-ended questions
Close-ended questions can be like “Do you like our coffee?”, or “Which is your favorite product?” However, questions that require detailed answers like “What’s your favorite way to have your coffee?”, or “What products would you most like to see on our menu?” can give you insights that ]allow you to see what your customers like about your business, what they don’t, and what you can do to improve it.
Because of the open-ended nature of these questions it can be challenging to make sense of the responses and measure the outcome of a survey that incorporates open-ended questions. This challenge can be solved with a survey analysis software powered through AI-powered natural language processing (NLP) algorithms.These powerful machine learning models can predict survey response patterns when conducting survey data analysis with semantics. They are able to do so by using information that is available before conducting the survey.
To get a satisfactory result in a survey, you’ll deal with latent variables. These are unmeasurable variables that have no simple “yes” or “no” answers and can influence the other aspects of your survey. So, to get a measurable result and reach a conclusion, the survey will often ask several questions related to the same topic. Based on the answers to those extra questions, AI algorithms can then predict what a customer’s response will be to semantically linear questions. This is what semantics plays a crucial role in automated sentiment analysis of survey data.
2. Identify semantic similarity
Although survey responses may sound alike, they could have different meanings which can impact the results of your survey data analysis. For example, the sentence “better menu selection” could mean either (a) to improve the menu selection; or (b) a positive sentiment as an answer to a query that says “why do you prefer this restaurant to another? Semantics enables the machine learning model to differentiate between these two kinds of sentiments in surveys, because there is no reference point when it comes to open-ended questions and answers. Watch this video to learn more.
3. Extract aspect sentiment without redundancy
Semantics in a survey analysis software ensures that sentiment is extracted from each aspect, feature, and entity redundancy. This means that the algorithm knows that “food” is the same aspect as “grub”. Semantics is very important in this kind of data that is consumer-driven because people tend to use informal and colloquial language when filling out forms or writing comments. Having a large and intensive vocabulary that includes colloquialisms, code switches, and abbreviations in multiple languages ensure that the insights extracted from the employee or customer data is precise and accurate.
4. Identify changes in sentiment over time
Semantics allows you to measure and keep track of changing sentiment around the identified aspects over time. The survey analysis software has the ability to compare historical and current data, and semantics allows the algorithm to know that “yeah, right” can be a positive sentiment as well as a negative one when said sarcastically. The algorithm will read the preceding sentence and the text after the phrase “yeah, right” to confirm whether the text in its entirety is positive or negative. If there is an emoji after the text, Repustate IQ is able to read the special character and that helps it identify the sentiment expressed more easily. And that’s why it is important that the survey software that you choose has the capability to extract special characters and emojis from the text, because most platforms ignore any other data that is not text or numerical.
5. Analyze data in all formats
A survey analysis software needs semantics for accurate results because data can come in various formats including video, audio, text, or image. Whether it is data at an organizational level where it can comprise marketing videos, product presentations, emails, sales collaterals, process documentations, conference videos; or user-generated data as in social media videos, comments, or posts, survey data analysis depends on semantics to ensure that all kinds of information are processed seamlessly so as not to leave out any details.
6. Identify aspect co-occurrence
Sentiment analysis allows you to identify recurring topics and aspect co-occurrences which can give you deeper insights. For example, semantics in a survey analysis software will be able to figure out that “air-conditioning” and “room” are occurring at a more frequent rate together, than say “room” and “tickets”. This is an aspect co-occurrence. Therefore, it will eventually put together that customers are talking about the “comfort” of their room - whether the air conditioning in the room was good, or whether they wish there was air-conditioning.
Understand semantic similarity in detail. Watch the video.
How Can You Choose the Best Survey Analysis Software?
Considering the above, it’s easy to see why you need survey analytics software that use both semantics and sentiment analysis to identify and measure your customers’ feedback when performing survey data analysis. So what features should you consider when choosing the right survey analysis software? There are:
1. Data security
Data security is vitally important, especially when conducting surveys and survey data analysis in industries like healthcare and finance. The chosen software should have the necessary features to ensure data privacy and safety.
2. Skip Logic
Skip logic only shows follow-up questions once a survey participant has answered a previous question. In other words, the follow-up questions will not be visible when a participant works through a survey which, in turn, makes the survey more appealing and less daunting.
3. Automatic periodic surveys
This feature allows you to send surveys to your customers automatically at certain times during their customer journey. This saves you a lot of time while ensuring that you get data for survey data analysis at regular intervals.
4. Feedback integration
The right survey analysis software should have the ability to incorporate feedback into your web and mobile applications automatically, seamlessly, and without your input.
5. Templates and design
The ideal machine model for survey data analysis should give you the ability to customize and create email templates based on your specific needs and requirements. This, in turn, makes it easier to create different designs for different target audiences.
6. Target audience
The right software should allow you to manage multiple different participants and gather feedback based on different parameters including demography, geography, age, and behaviors.
7. Training and support
The survey data analysis platform should have excellent training and support. It should also have extensive resources like blogs, guides, and more, that will allow you to use the platform effectively.
8. Visual dashboard
Last, but not least, the platform should have a sentiment analysis dashboard where you can see all your insights one one single screen for all the aspects and entities the algorithm captures from the customer data.
Repustate for survey data analysis understands the semantic relationship between texts and phrases regardless of language. Watch this video to understand more.
Through its powerful sentiment analysis API, Repustate IQ, the all-in-one sentiment analysis platform for customer experience, can extract sentiment from surveys that can be in any format - videos, audio, or text. The platform gives you the ability to analyze your data as in-depth as you want, and its sentiment analysis features allow you to conduct topic-based sentiment analysis or aspect-based sentiment analysis for deeper insights and a more holistic view for brand intelligence.