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Sentiment Analysis AI - How Artificial Intelligence Predicts Customers' Emotions

Sentiment Analysis AI - How Artificial Intelligence Predicts Customers' Emotions

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Zummit Africa
·Nov 14, 2022·

5 min read

Table of contents

Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative, or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

Sentiment analysis is the process of detecting positive or negative sentiments in text. It’s often used by organizations to detect sentiment in social data, gauge their reputation, and understand their customers.

Types of Sentiment Analysis

The following are some types of sentiment analysis:

Graded Sentiment Analysis

If the precise polarity is important to your business, you might consider expanding your polarity options to include different grades. This can include:

  • Very positive

  • Positive

  • Neutral

  • Negative

  • Very negative

This is usually referred to as graded sentiment analysis. You can use this to interpret ratings in a customer review. For instance:

Very Positive = 5 stars Very Negative = 1 star

Emotion detection

Emotion detection sentiment analysis allows you to go beyond negative and positive grades to detect emotions, like happiness, frustration, anger, and sadness.

Many emotion detection systems use lexicons (i.e. lists of words and the emotions they convey) or complex machine learning algorithms.

One of the downsides of using lexicons is that people express emotions in different ways. Some words that typically express anger, like bad or kill (e.g. your product is so bad or your customer support is killing me) might also express happiness (e.g. this is a badass or you are killing it).

Aspect-based Sentiment Analysis

Usually, when analyzing sentiments of texts you’ll want to know which particular aspects or features people are mentioning in a positive, neutral, or negative way.

Multilingual sentiment analysis

Multilingual sentiment analysis can be difficult. It involves a lot of preprocessing and resources. Most of these resources are available online (e.g. sentiment lexicons), while others need to be created (e.g. translated corpora or noise detection algorithms), but you’ll need to know how to code to use them.

Alternatively, you could detect the language in texts automatically with a language classifier, then train a custom sentiment analysis model to classify texts in the language of your choice.

Applications of Sentiment Analysis

The following are some applications:

Social Media Monitoring

Sentiment analysis is used in social media monitoring, allowing businesses to gain insights into how customers feel about certain topics, and detect urgent issues in real-time before they spiral out of control.

Brands of different shapes have meaningful interactions with customers, leads, and even their competition, all across social media. By monitoring these conversations you can understand customer sentiment in real-time and over time, so you can detect disgruntled customers immediately and respond as soon as possible.

Most marketing departments are already tuned into online mentions as far as volume – they measure more chatter as more brand awareness. But businesses need to look beyond the numbers for deeper insights.

Brand Monitoring

Not only do brands have a wealth of information available on social media, but across the internet, on news sites, blogs, forums, product reviews, and more. Brand monitoring offers a wealth of insights from conversations happening about your brand from all over the internet. You can analyze news articles, blogs, forums, and more to gauge brand sentiment and target certain demographics or regions, as desired. Real-time sentiment analysis allows you to identify potential PR crises and take immediate action before they become serious issues. Or identify positive comments and respond directly, to use them to your benefit.

Voice of Customer (VOC)

Social media and brand monitoring offer us immediate, unfiltered, and invaluable information on customer sentiment, but you can also put this analysis to work on surveys and customer support interactions.

Businesses use these scores to identify customers as promoters, passives, or detractors. The goal is to identify the overall customer experience and find ways to elevate all customers to the “promoter” level, where they, theoretically, will buy more, stay longer, and refer other customers.

Sentiment analysis can be used on any kind of survey – quantitative and qualitative – and on customer support interactions, to understand the emotions and opinions of your customers. Tracking customer sentiment over time adds depth to help understand why NPS scores or sentiment toward individual aspects of your business may have changed.

You can use it on incoming surveys and support tickets to detect customers who are ‘strongly negative’ and target them immediately to improve their service. Zero in on certain demographics to understand what works best and how you can improve. You can read more about this here.

Customer Service

Quality customer experiences mean a higher rate of retaining customers. Customers expect their experience with companies to be immediate, intuitive, personal, and without problems. If not, they’ll leave and do business elsewhere.

You can analyze customer support interactions to ensure your employees are following appropriate protocol. Make sure you increase efficiency, so customers aren’t left waiting for support. Decrease churn rates; after all, it’s less problem to keep customers than acquire new ones.

Market Research

Sentiment analysis makes market research and competitive analysis easy. Whether you’re exploring a new market, anticipating future trends, or seeking an edge over the competition, sentiment analysis can help your business.

You can analyze online reviews of your products and compare them to your competition. Maybe your competitor released a new product that landed as a flop. Find out what aspects of the product performed most negatively and use them to your advantage. With social data analysis, you can fill in gaps where public data is scarce, like in emerging markets.


With sentiment analysis AI, you can improve your business growth.

Would you like to build sentiment analysis API for your organization? We are available to meet your business needs. Contact us today!

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