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How To Enhance Your SaaS Product With A Sentiment Analysis API

A sentiment analysis SaaS (software as a service) tool automatically discovers and detects emotions in your customer experience (CX) data to not only help you enhance your products but also keep you a step ahead of your competitors. It helps product managers make critical data-driven decisions that fuel their leads pipeline. And it allows businesses to understand their audience better and evaluate the attitude and sentiment drivers of their target audience. This is how SaaS products like Netflix, Dropbox, Gmail, and the like have grown into the mammoth enterprises they are today.

In this article, we discuss what makes a sentiment analysis SaaS model apt for extracting insights from CX data, and how you too can use it to your advantage.

What Is Sentiment Analysis SaaS?

A sentiment analysis SaaS model uses machine learning to evaluate emotions expressed in data that you can use for product monitoring, market research, and brand positioning. An advanced sentiment analysis API can extract sentiment not just from traditional text data but all data types including videos. This empowers you to analyze sentiment in text mediums such as product surveys, social media listening data, product review platforms such as Clutch, TrustPilot, or even Reddit, as well as evaluate user-generated videos on platforms like Vimeo, YouTube, or Douyin.

Continuous product and service evolution through analyzing sentiment in customer feedback, spread across channels, is what makes a business recession-proof. And that is exactly what a sentiment analysis API enables you to do through the insights it extracts from customer experience analysis data.

How Does A Sentiment Analysis API Extract In-Depth Insights?

Sentiment analysis SaaS uses aspect-based sentiment analysis (ABSA) to extract granular insights about a product through voice of the customer analysis. ABSA employs several machine learning (ML) tasks such as natural language processing (NLP), semantic clustering, aspect similarity co-occurrence, and the like, to identify product attributes mentioned in the CX data, and then analyzes the emotions related to them.

For example, if there is a Tweet where a customer has expressed their review of a DSLR camera, the API will not only determine the sentiment around the product, i.e. the camera, but also search for any sentiments expressed about its attributes or aspects such as exposure compensation, shutter speed, or lens. A powerful sentiment analysis API, governed by robust artificial intelligence, can process thousands of such comments in seconds.

A bit more on the ML tasks that comprise aspect-based sentiment analysis.

  1. Semantic clustering

Semantic clustering ensures that there is no redundancy of data and that all semantically equivalent words and phrases are clubbed together. ML algorithms detect and identify the topics, and semantic clustering pulls all semantically similar content together for sentiment analysis. This feature in a sentiment analysis SaaS model protects you from false positives and negatives.

  1. Feature extraction and analysis

NLP and semantics identify product features in a body of text, which are extracted for sentiment analysis. For example, the API will extract features like “gift card management” and “menu” from SaaS-based food delivery app solutions like OpenTable or Dessert POS, and separately analyze sentiment for each feature.

  1. Aspect co-occurrence

Aspect co-occurrence

Aspects of a product or service that occur together often in the data are extracted to analyze the sentiments expressed in them. This allows product managers to understand why these aspects occur together and what exactly about them is leading to an increase or decrease in positive or negative sentiment.

  1. Emotion-aspect co-occurrence

Emotion aspect co-occurrence

You can know which emotions occur frequently with which aspects on a regular basis. This tells you clearly what you can improve about your product/service and what is going really well.

Read more about the features of aspect-based sentiment

How Can I Use A Sentiment Analysis API To Enhance My SaaS Product?

Powered by machine learning, a sentiment analysis platform can give you high-precision CX insights that you can use to improve your product, better customer retention, understand sentiment drivers, track online chatter for brand reputation management, and so much more. Let’s get to know these advantages in detail.

1. Understand your market

This is the most important of all things for a product manager and for a brand as a whole. To know your customers is the key to success, whether you are a small business or an enterprise. Take for example, the e-signature company, DocuSign. They were amongst the first to launch a SaaS product such as theirs at a time when not many were sure of using electronic signatures for fear of data tampering and faulty encryption. Unperturbed by the lack of major success, Docusign continued listening to the market and now has more than 100 million users completing one million transactions per day. DocuSign again rose to the occasion during the covid19 pandemic when all industries had to resort to digital attestations and signatures due to lockdowns and social distancing rules.

2. Track millions of social conversations

A sentiment analysis API enables you to analyze millions of comments on social media channels, product review websites, news articles, and more. You can extract key information through social media video content analysis on platforms like TikTok, Douyin, YouTube, and even Facebook. Qualitative analysis of this data can tell you exactly what people are talking about in the comments, which can be immensely valuable for product and service improvement.

Read how Repustate’s sentiment analysis solution identified key information about two Wendy’s locations from social chatter.

3. Retention Analysis

Sales and marketing teams need to do a comprehensive retention analysis to find out what it takes to retain customers - which is just as important as gaining new customers. A Sentiment analysis SaaS model can throw valuable light onto facets that can help improve customer retention.

4. Analyze User Engagements

User engagement is a crucial component of a marketing function. Do your customers prefer a pay-as-you-go model or are they more comfortable with an on-premise installation? Are you providing your customer enough hand-holding during the early product purchase stages? Are you up-selling to your customers at the right time. A sentiment analysis API helps you answer these and other user engagement aspects so that you can formulate an effective strategy.

5. Scrutinize Sentiment Drivers

What is it that makes your SaaS product attractive to buyers? This is an essential question that you must answer before you get to the stage where you carve out a strategy for product improvement. When Australian SaaS design company Canva made its debut in 2011, it had done a thorough market gap analysis to understand sentiment drivers for its product. It was cognizant of the fact that there were millions of businesses that were unhappy at being dependent on design agencies or having to hire graphic designers even for the simplest design job such as a pamphlet or social post graphic. Today, Canva has a user base of 55 million subscribers who couldn’t be happier thanks to a platform that allows them to be self-sufficient.

6. Discover Insights From In-App Surveys

Sentiment analysis SaaS provides you a fundamental way to extract accurate information from in-app surveys. The trick to getting the most out of a survey is to have open-ended questions in them. But these open-ended answers are what trump most survey analysis tools because there is no benchmark against which they can analyze the answers. Repustate’s AI-driven survey sentiment analysis through semantic clustering and a frame of reference that disambiguates words and phrases. This intelligent feature is what helps the API decode survey answers even if it doesn’t know what the questions are.

Learn more.

7. Benchmark Against Competitors

Extracting insights from your customer experience data is not just for your own products but is also pertinent in helping you understand your competitors better. When you analyze social media data, you can train the sentiment analysis SaaS model to recognize and extract features, aspects, and entities from any comment that mentions your competitor brands as well. With sufficient training, the models become smarter with time as they process more and more data, and eventually give you high-precision insights that are hidden from plain sight in the deluge of data.

8. Drive Adoption

Knowing buyer behavior and understanding sentiment trends in your data can point you in the right direction in your strategy to drive adoption of your SaaS product. This is the reason why Netflix is more successful than cable television and other digital platforms like Hulu or Prime.


Repustate’s sentiment analysis API gives you aspect-driven insights that are granular, targeted, and highly precise. The API can process more than 1000 comments per second, and can scale from 1 to 10 million to 100 million documents and beyond. What’s more, it is highly customizable and available in aspect models that are specific to your industry. This means that whether you are a SaaS company in hospitality, healthcare, education, or entertainment, Repustate’s sentiment analysis SaaS model can be trained for insights based on your exact requirements.

With 23 languages that it can analyze natively and features such as search inside video, the API is the perfect match for product innovation and improvement needs.

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