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GuidesCustomer researchSentiment analysis: Find out what users think about your brand & products

Sentiment analysis: Find out what users think about your brand & products

Last updated

14 February 2023

Reviewed by

Cathy Heath

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Do you know exactly how your target audience feels about your business?

To thrive, your company needs an in-depth understanding of its customers. Beyond exploring their experience shopping with and using your brand, it’s equally important to study how your customers feel about your business.

Thankfully, there are systematic ways to collect information about your customers' opinions. Through detailed sentiment analysis, you can collate valuable data about positive, negative, and emotional responses that your target audiences hold about your brand.

Whether you’re looking to expand to a new market or trying to get to the bottom of a string of negative social media comments, sentiment analysis is a must-use tool. 

It’s perfect for informing your team about how your target audience sees your products and services.

Ready to take customer research to the next level? This article covers the essential features of sentiment analysis, including how it can enhance your business offerings and encourage sustainable business growth.

What is sentiment analysis?

Sentiment analysis (also known as emotions AI, opinion mining, or affective rating) systematically analyzes and classifies text to determine a tone of positivity, negativity, or neutrality. Simply put, it is the process of using computerized systems to determine the emotional tone and context of words used in customer feedback.

Sentiment analysis tools look for particular words and phrases that convey tone and emotion. 

These tools can analyze sentiment by combing through social media posts, Google reviews, responses to customer satisfaction surveys, and more. 

So, what does this software look for?

Learn more about sentiment analysis software

Positive sentiment

This is when a customer enjoys or appreciates your product or service. They often express their appreciation with words like “good,” “great,” “amazing,” “fantastic,” and “awesome.” 

In most cases, the more positive feedback that your business receives, the more in touch you are with your desired audience.

Negative sentiment

Negative sentiment comes with harsh and emotional language in feedback. Examples of words that imply negative sentiment include “bad,” “gross,” “difficult,” “terrible,” and “disappointing.” 

You can find these emotionally-charged words in customer reviews, social media rants, and responses to product surveys.

Neutral sentiment 

Neutral sentiment is harder to discern because it is not a particularly emotionally-charged tone. Responses including words like “ok,” “alright,” and “fine” are examples of customer feedback that you could consider neutral. 

In most cases, this is the least common form of feedback a business receives because very few people leave mid-tier reviews for products or services.

Don’t forget how slang can play into feedback: Words such as “wicked” or “bad” can sometimes mean the opposite!

What is natural language processing (NLP) sentiment analysis?

To speed up the process of churning through thousands of pieces of unstructured customer feedback, natural language processing (NLP) sentiment analysis can be a helpful strategy.

Natural language processing is a complex interdisciplinary field that combines computer science, artificial intelligence, and linguistics. It teaches software systems how to interact, understand, and rate the emotion and nuances of human language.

NLP sentiment analysis software trains computers to identify and contextualize human words and phrases, saving time and energy throughout the analysis process. 

Depending on the type of information that your company is looking to gain, you can use NLP sentiment analysis for several purposes:

  • Identifying emotional tone

  • Qualifying the extent of positivity and negativity

  • Identifying specific, granular emotions such as happiness, disappointment, and anger 

Often, companies pay NLP sentiment analysis services to provide software for these tasks, offloading the work of creating and training these systems from internal resources.

What is a sentiment score?

A sentiment score is one of the metrics you get from sentiment analysis. Often on a numeric scale, a sentiment score comes from your brand’s positive or negative feedback.

One of the most common types of sentiment scores is grading customer feedback on a scale from one to ten, from most negative to most positive. 

Depending on the type of information that you are looking to collect, sentiment scores can value information such as:

  • The number of positive or negative ratings 

  • The emotional strength of each writing piece (strongly enjoyed vs. intensely disliked)

  • The context surrounding the feedback, like the timing and reasoning behind the rating

Using your sentiment score as a guide, your company will have a clearer idea of which path to take moving forward. For example, if your score is lower than you wanted, you know that you need to focus on customer satisfaction and adjusting your product/service/user experience. 

If your score is higher, your business is meeting your customer’s expectations. Still, there are always areas to improve.

Example data sources for sentiment analysis

Depending on the information you want to collect, you can analyze different datasets to provide a deeper understanding of the general sentiment of your customers. Examples of common data sources for sentiment analysis include:

Survey text analysis

Have you recently sent out a customer satisfaction survey? This is the perfect opportunity to try sentiment analysis! You can use a sentiment analysis tool to evaluate survey feedback and report on your customers’ most commonly felt emotions toward your brand.

Customer review text analysis

Whether you collect customer reviews directly on your website, after a purchase, or from Google reviews, this is a goldmine of valuable sentiment information. Reviews are fascinating data sets because they are very polarizing — often very positive or negative.

Social media text analysis

As social media platforms like Twitter, Facebook, Instagram, and LinkedIn continue to rise in popularity, so do unprompted conversations about your brand. 

Your business can better understand your brand’s day-to-day sentiment changes using live tracking sentiment analysis tools. This is particularly helpful during product launches, website redesigns, or when a controversy or crisis affects your brand or service.  

What is sentiment analysis used for?

As the interest in customer satisfaction and experience increases across all industries, more companies of all sizes are integrating sentiment analysis into their user research practices.

Whether your business is in eCommerce, marketing, manufacturing, or market research, sentiment analysis can offer a wide variety of benefits:

Brand reputation management

Your brand is your reputation — are you doing enough to protect it from negative publicity?

Managing a brand is a complex, multi-faceted process. A significant portion of the public opinion of your brand comes from the value and experience of using your products or services. However, other factors like website usability, search engine optimization, and social media presence influence your customers' perception of your brand. 

Your company can run a sentiment analysis to keep tabs on what the general public and your target audience are saying about your brand. Look at social media posts, published articles, and general think-pieces that mention your brand in any way. Insight into the tone of feedback your company is getting can support data-driven decision-making.

Marketing research

No matter how long your company has been in business, conducting thorough marketing research to better understand your competitors and customers is part of a winning brand strategy.

As a great tool for providing detailed information about specific markets, niches, and customer spending habits, sentiment analysis helps you quickly and efficiently identify trends. If your business is looking to launch a new product or enter a new market, conducting a thorough sentiment analysis is one of the best ways to encourage long-term success.

Processing customer feedback

If your company is already using customer satisfaction surveys as part of your user research process, sentiment analysis can help you get even more information from your feedback.

Depending on the type of questions you’re using in your surveys, a sentiment analysis tool can parse the tone and emotion behind each submission. This style of data analysis is particularly helpful for surveys with open-ended text questions, where customers can freely type their feedback.

A sentiment analysis program can go through survey answers and provide insights into your customers’ general tone to determine whether your customers are happy. Once complete, this information will be essential in improving and adjusting your offers to best meet the needs of your target audience.

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Crisis prevention and political management

We all know the saying, “any press is good press,” but it’s usually best to avoid negative publicity.

In the modern age of social media and trending topics, companies can easily catch unwanted negative attention seemingly overnight. 

In the event of a brand crisis, it’s vital to quickly identify negative sentiment trends by live-tracking social media and public contact form posts. High-quality sentiment analysis tools can keep your brand one step ahead of any swells of incoming negative feedback.

Learn more about sentiment analysis software

Types of sentiment analysis

When starting a sentiment analysis project for your business, it is vital to know the different types and methods to get the specific results you need. 

Examples of some of the most effective types of sentiment analysis include:

Graded sentiment analysis

One of the most common types of sentiment analysis involves grading or numerically scaling the positive or negative sentiment in your data.

While filtering and processing the collected information, sentiment analysis tools can create a numerical value that represents the specific metric you are measuring. 

For example, graded sentiment analysis can create:

  • Sentiment scores on a scale from 1–10, ranging from negative to positive feedback

  • A star rating system, indicating happiness with your product/service from 1–5 stars

  • Positivity or negativity percentages, based on a set group of feedback

  • Ranges of descriptors such as “very positive” or “somewhat negative” with a product or service offering

Aspect-based sentiment analysis

Product and service troubleshooting is an essential area that sentiment analysis can assist with.

You may have minor hitches when your business launches a new offering. Perhaps your customers can’t access their accounts, or your website has 404 errors. Whatever the issue, your business must be able to correctly identify it to find the solution.

Using aspect-based sentiment analysis, your company can collect and interpret valuable information about these events. This helps you identify trends and areas requiring additional assistance and tweaking. 

Examples of situations where aspect-based sentiment analysis is incredibly valuable include:

  • Customers reporting a payment error when trying to purchase your new product

  • Website errors reported to your customer service chatbot

  • Subscription or service cancellations based on a bad experience with a staff member

Multilingual sentiment analysis

Does your company have international reach? If so, it’s essential to consider the many languages used by your customer base when collecting valuable data about brand reputation.

Depending on the broadness of your target audience, cultural and language differences can significantly impact the type of data and feedback you will receive. 

While it is essential to collect this information to see how your brand is performing in different cultural and geographic locations, it poses challenges. Multilingual sentiment analysis can come up against issues when translating and examining the true meaning of customer feedback.

To tackle this issue, you can train multilingual sentiment analysis tools to perform the following essential steps to successful language translation:

Speech tagging

This is the process of identifying and labeling nouns, verbs, and descriptive, emotional words in each sentence. It’s the first step to understanding the tone and content of each sentence.

Word root identification

Also referred to as lemmatization, a language-specific tool analyzes identified words to better understand their root. 

For example, the words “drinking,” “drank,” and “drunk” come from the root word “drink.” Once the tool categorizes these words, the sentiment analysis tool better understands the sentence context.

Prior polarity

A sentiment analysis tool can search for words’ intent and emotional context. Words like “love” and “despise” highlight a strong emotional meaning: Something that creates a polarizing effect. 

Identifying these words helps the system interpret the feedback’s emotional tone and context.

Grammar assessment

Every language has a set list of grammatical rules so speakers can understand each other. Our languages range in complexity from minor sentence structure choices to contextual sarcasm and humor. 

That means multilingual sentiment analysis must be able to identify and understand the unique grammar quirks of each language.

Machine learning

Once you’ve completed all these steps, machine learning tools can compile the data into a coherent score or statistic for your company. In most cases, this is a positive or negative numerical score, indicating the emotional response to the area you are studying.

Emotion detection

As it says in the name, this style of analysis hyper-focuses on gaining information about the underlying emotions and experiences of your customers and target audience.

Instead of creating a numeric scale to represent the results, this sentiment analysis uses emotional words or images to create a more inclusive, broad result. 

Emotion detection sentiment analysis works best on longer written feedback from social media or feedback surveys. Through the analysis, the tool can highlight buzzwords and keywords, helping your team better understand how people perceive your brand and the level of engagement. 

This type of sentiment analysis can be beneficial in the following areas of business expansion:

  • Content creation and rating: Which blog topics connect with people the most?

  • Website redesign: Are your customers enjoying the design changes you made?

  • After a new product launch: Did your target audience respond well to your offering? 

Using sentiment analysis to grow your business

Is your business looking to expand, change, or explore new business opportunities and markets? Starting strong with detailed sentiment analysis is an essential step to success.

How customers feel about your brand is more important than many people realize. 

No matter how great, amazing, or revolutionary your offerings are, if your target audience finds reasons to dislike your brand, they will not spend money with your company. They may have political, emotional, or user-experience-based reasons. Whatever their reasons may be, you need to identify these sticking points. 

Sentiment analysis is a key strategy for your user research plan. It’s the best way to combat any developing trends of negativity, learn more about pain points, and lean into what your customers really enjoy.

Learn more about sentiment analysis software

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