Businesses are constantly striving to improve customer experience (CX). Sentiment analysis tools have become indispensable for companies aiming to understand and respond to their customers’ feelings and needs. Whether it's gauging customer satisfaction, monitoring brand reputation, or gathering insights from social media, sentiment analysis can provide a wealth of data-driven insights.

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However, choosing the right sentiment analysis tool can be challenging, given the variety of options available and the complexity of sentiment analysis technology. In this buyer’s guide, we’ve curated practical, expert tips on what to look for when selecting a sentiment analysis tool and how to get the maximum value from your investment.

Read on to learn more about:

• What to look for when choosing a sentiment analysis tool
• How to get the most value from your sentiment analysis tool
• Frequently asked questions

What to look for when choosing a sentiment analysis tool

1. Choose a sentiment analysis tool that considers acoustic measurements. “Language is complex, and as a process for quantifying and scoring language, sentiment analysis is equally complex. What is relatively easy for humans to gauge subjectively in face-to-face communication, such as whether an individual is happy or sad, excited or angry, about the topic at hand, must be translated into objective, quantifiable scores that account for the many nuances that exist in human language, particularly in the context of a discussion. For instance, a word that otherwise carries a positive connotation used in a sarcastic manner could easily be misinterpreted by an algorithm if both context and tone are not taken into consideration.

“Given these challenges, customer analytics software vendors must consider acoustic measurements (the rate of speech, stress in a caller’s voice, and changes in stress signals) in the context of the conversation. Additionally, integrating machine learning into the mix enables sentiment analysis to become more accurate over time, as algorithms learn and adapt to the commonalities in conversations and how the context of conversations can change outcomes.” - What is Sentiment Analysis? Examples, Best Practices, & More, CallMiner; Twitter/X: @CallMiner

2. Look for a user-friendly sentiment analysis tool. “The sentiment analysis tool you choose shouldn’t require excessive training to function properly. All NLP tools are not created equally. Some models require manual training to label language and detect industry-specific topics accurately.

“Hence, you should select a sentiment analysis software that has a quick and easy setup process and uses state-of-the-art technology to minimize the efforts required on your side.

“If you are planning on analyzing large datasets, ensure that the tool you select has the ability to handle it in order to avoid lagging technology prone to crashing.” - 10 Sentiment Analysis Tools Marketers Should Use In 2023, Statusbrew; Twitter/X: @Statusbrew

3. Consider sentiment analysis tools you can integrate with your existing technology. “Not all companies can afford to build custom ML models for sentiment analysis. Fortunately, there are various off-the-shelf tools that collect feedback from numerous sources, alert on mentions in real time, analyze text, and visualize results. Some of these platforms expose APIs so you can integrate them with your existing system and get access to sentiment analysis instruments directly from your working environment.” - Sentiment Analysis: Types, Tools, and Use Cases, AltexSoft; Twitter/X: @AltexSoft

4. Look for a sophisticated sentiment analysis tool that can understand context. “Sentiment classification requires your sentiment analysis tools to be sophisticated enough to understand not only when a data snippet is positive or negative, but how to extrapolate sentiment even when both positive and negative words are used. On top of that, it needs to be able to understand context and complications such as sarcasm or irony.

“Human beings are complicated, and how we express ourselves can be similarly complex. Many types of sentiment analysis tools use a simple view of polarity (positive/neutral/negative), which means much of the meaning behind the data is lost.” - Sentiment analysis and how to leverage it, Qualtrics XM; Twitter/X: @Qualtrics

5. Emotion detection sentiment analysis goes further than standard sentiment analysis. “Emotion detection sentiment analysis is a bit more nuanced than standard. “As you can probably deduce from the name, emotion detection sentiment goes beyond positive, negative, and neutral sentiment to detect emotions like happiness, anger, frustration, sadness, and others. Emotion detection sentiment analysis tools typically use lexicons or machine learning to accurately detect emotion with machine learning providing a more accurate measure.” - Koba Molenaar, Your Guide to Sentiment Analysis [+ Top Tools to Use], Influencer Marketing Hub; Twitter/X: @influencerMH

6. Choose a sentiment analysis tool that offers machine learning-based emotion detection. “Emotion detection determines emotions such as joy, sadness, fear, worry, etc. It uses lexicons (set of words and expressions) that identify specific emotions and machine learning-based classifiers. As humans express feelings in various ways, ML-based emotion detection is preferred over lexicons.

“For example: ‘This phone is just insane.’ Such a review may confuse the sentiment analysis model as it may evoke two different sentiments. One may be entirely positive, while the lexicon ‘insane’ may classify it as one denoting fear or panic. Thus, it may give inaccurate results if only lexicons are used. However, with ML-based detection, such a possibility is avoided.” - Vijay Kanade, What is Sentiment Analysis? Definition, Tools, and Applications, Spiceworks; Twitter/X: @SpiceworksNews

7. Consider a conversation intelligence platform, which captures and analyzes feedback across channels. “Businesses have traditionally used surveys to measure brand sentiment. However, surveys often suffer from dismal response rates that result in an incomplete picture of brand sentiment. Additionally, surveys are prone to bias, since respondents are often motivated by highly negative or positive opinions.

“Recently, brands have turned to social media listening tools to monitor comments on social sites and online reviews. While this approach offers a much larger sample size, results may still be skewed by the fact that most people posting comments are either very satisfied or dissatisfied with their brand interactions. Plus, relying solely on social media monitoring ignores all the other channels where customers reveal sentiment about a brand.

“For this reason, many companies have chosen conversation intelligence technology that can capture and analyze the unsolicited feedback from more customers, offered daily on a broad range of channels.” - How Do Brands Use Sentiment Analysis?, CallMiner; Twitter/X: @CallMiner

It may seem tempting to use generative #AI to build a homegrown conversation intelligence solution.

Our whitepaper looks at the risks and costs in this approach, and why a licensed solution can deliver greater value in driving contact center, CX and business improvements. ⬇️

— CallMiner, Inc. (@CallMiner) December 20, 2023

8. Look for a solution that goes beyond quantitative metrics. “Customer feedback metrics like net promoter score (NPS), customer effort score (CES), or star ratings can tell you at a glance whether people are happy with your business or not. But this doesn’t really give you any actual business insight.

“To get real customer sentiment insights you need to go beyond quantitative metrics. And for that, you need to analyze comments and open-ended survey responses that do not have any fixed response. This allows customers to write free-flowing comments, which can give you insight into aspects of your business that you were not even aware of.” - Martin Ostrovsky, How To Collect Data For Customer Sentiment Analysis, KDnuggets; Twitter/X: @kdnuggets

9. Deep learning leverages neural networks designed to function like the human brain. “The basic level of sentiment analysis involves either statistics or machine learning based on supervised or semi-supervised learning algorithms. As with the Hedonometer, supervised learning involves humans to score a data set. With semi-supervised learning, there’s a combination of automated learning and periodic checks to make sure the algorithm is getting things right.

“Deep learning is another means by which sentiment analysis is performed. ‘Deep learning uses many-layered neural networks that are inspired by how the human brain works,’ says IDC’s Sutherland. This more sophisticated level of sentiment analysis can look at entire sentences, even full conversations, to determine emotion, and can also be used to analyze voice and video.” - Maria Korolov, What is sentiment analysis? Using NLP and ML to extract meaning, CIO; Twitter/X: @CIOonline

10. Look for a sentiment analysis tool that can account for outliers and sarcasm. “The volume of sentiment-related terms in your searches doesn’t always tell the full story of how your customers feel. It’s crucial to double-check your mentions and leave some room for analytical error.

“Here’s a good example from Netflix’s Facebook page. Fans are obviously singing the praises of their programming, but they’re also throwing in terms like ‘ugly,’ ‘cry’ and ‘depressed’ while doing so. If you saw those terms pop up in your mentions without context, it might be cause for alarm.

“Sarcasm can likewise create confusion when it comes to sentiment analysis. When somebody Tweets ‘I love it when I lose my luggage after a nine-hour flight,’ they obviously aren’t thrilled about their experience.

“Although sentiment analysis is going to be accurate most of the time, you’re always going to have these sorts of outliers. A combination of manual listening and machine learning is ideal for getting the most ‘complete’ sentiment analysis possible.” - Brent Barnhart, The importance of social media sentiment analysis (and how to conduct it), Sprout Social; Twitter/X: @SproutSocial

11. Consider unsolicited feedback. “Each customer expresses her sentiment differently and in various places, whether it be by direct or indirect feedback. Both kinds are equally important to consider. If your customer feels strongly enough to take the time to email you, reach out to your customer support via chat , or fill out a customer survey, her directly communicated sentiment is valuable and contains numerous sentiment indicators.

“On the other hand, the internet has become a beloved hub for customers around the world to share their experiences and unfiltered opinions. And these indirect sentiments have the power to reach audiences in the millions.

“Maybe you remember the United Airlines scandal? Their personnel were way too rough with baggage and broke a musician’s guitar — and then refused to pay for the damages. The customer was rightfully disappointed and wrote a viral song about it called ‘United Breaks Guitars’ - which dealt a major blow to their image.” - Alina Günder, How to Track Customer Sentiment and Become a Master of Emotion, Userlike; Twitter/X: @userlike

12. Look for a sentiment analysis tool that detects negation. “In linguistics, negation is a way of reversing the polarity of words, phrases, and even sentences. Researchers use different linguistic rules to identify whether negation is occurring, but it’s also important to determine the range of the words that are affected by negation words.

“There is no fixed size for the scope of affected words. For example, in the sentence ‘The show was not interesting,’ the scope is only the next word after the negation word. But for sentences like ‘I do not call this film a comedy movie,’ the effect of the negation word ‘not’ is until the end of the sentence. The original meaning of the words changes if a positive or negative word falls inside the scope of negation—in that case, opposite polarity will be returned.

“The simplest approach for dealing with negation in a sentence, which is used in most state-of-the-art sentiment analysis techniques, is marking as negated all the words from a negation cue to the next punctuation token. The effectiveness of the negation model can be changed because of the specific construction of language in different contexts.” - Rudolf Eremyan, Four Pitfalls of Sentiment Analysis Accuracy, Toptal; Twitter/X: @toptal

How to get the most value from your sentiment analysis tool

13. Sentiment analysis can help you better understand your audience. “Marketers do their best work when they understand their audience. That means you need to understand how your audience feels about your brand, your social posts, and your campaigns, not just how much they mention you.

“For example, White Castle used social listening and sentiment analysis to discover that their customers have a positive association with the very specific experience of eating White Castle sliders while watching TV in bed.

“With this knowledge in hand, White Castle featured a couple eating sliders in bed in their next campaign.” - Christina Newberry, Social Media Sentiment Analysis: Tools, Tips, and More, Hootsuite; Twitter/X: @hootsuite

14. Leverage sentiment analysis to optimize the user experience. “User experience is not only about how your product solves customers’ problems but also how they feel during the entire journey with you. The best way to understand it is by analyzing customer feedback.

“Using a customer sentiment analysis tool, you can act on positive and negative customer feedback. This allows you to fix the underlying issues and create a better experience for your users.” - Customer Sentiment Analysis: What Is It and How To Collect Data for It [+Best Tools], Userpilot; Twitter/X: @teamuserpilot

15. Sentiment analysis can be applied to survey results to extract deeper meaning. “Survey sentiment analysis is the process of sorting customer feelings behind answers to open-ended survey questions—into categories like positive or negative, or certain themes like the user experience (UX)—and then analyzing those sentiments. This is key to measuring a user’s emotional connection to your brand and product. The more connected they feel, the happier they are with your product experience (PX). And the happier they are, the more likely they are to buy, use, or recommend it.

“Ask a question like, ‘How do you feel about our product?’ and customers are likely to answer using words like ‘love’, ‘hate’, etc. So the sentiment will be fairly clear. However, if you ask, ‘What’s stopping you from recommending our product to a friend?’ it can be harder—but just as important—to decipher the customer’s sentiment behind their words.

“Survey sentiment analysis lets you uncover hidden insights behind your customers’ words, helping you empathize with them—and make changes to your branding, product, and processes to boost customer satisfaction and retention.” - Survey sentiment analysis: how to analyze the sentiment behind your survey responses, Hotjar; Twitter/X: @hotjar

16. Incorporate sentiment analysis in product development. “Sentiment analysis is an important way for organizations to understand how customers perceive and experience their products and brands. Increasingly, customer feedback is given online through a variety of unconnected platforms, such as Amazon product reviews and posts on social media platforms.

“Organizations typically don't have the time or resources to scour the internet and read and analyze every piece of data relating to their products, services and brand. Instead, they use sentiment analysis algorithms to automate this process and provide real-time feedback.

“Organizations use this feedback to improve their products, services and customer experience. A proactive approach to incorporating sentiment analysis into product development can lead to improved customer loyalty and retention.” - Nick Barney, Sentiment analysis (opinion mining), TechTarget; Twitter/X: @TechTargetNews

17. Use emotion analysis in conjunction with sentiment analysis. “ Businesses can weave emotion analytics into customer contact points, like emails, chatbots, and social media, to monitor brand perception. Emotion analytics and sentiment analysis work well together here, offering a general opinion of the brand as well as key emotions driving that opinion.

“For example, emotion analytics can monitor social media mentions about a brand to identify the most powerful emotions behind those mentions, like anger or excitement.” - How emotion analytics can benefit your business, CallMiner; Twitter/X: @CallMiner

18. Use sentiment analysis to predict future sales and identify risks and opportunities. “By monitoring customer opinions on products, services, or brands, sentiment analysis can help businesses predict future sales and identify potential risks and opportunities. For example, if sentiment analysis reveals a negative sentiment towards a particular product or service, businesses can address the issue and improve customer satisfaction.” - Igor Tomych, How To Make Informed Business Decisions With Sentiment Analysis In Financial Forecasting, DashDevs; Twitter/X: @DashDevs

19. Leverage sentiment analysis to conduct market research. “Users can make use of the valuable insights into market trends and customer preferences provided by AI-powered systems. These AI-driven sentiment analyzers can also identify emerging trends from unstructured data sources like social media and online forums to further aid in market research.” - Lizzy Lozano, 5 Best AI Sentiment Analysis Tools (Easy to Use), UPDF

20. Improve customer support by using sentiment analysis. “If you’ve ever seen the movie Office Space, the phrase, ‘Hey Peter, what’s happening’ likely triggers your own memories of overbearing bosses. But what if your support agents are using language that, unbeknownst to them, makes your customers want to crawl under their desks (or tables)? Customer sentiment analysis can help you ferret out language your customers find off-putting, whether that language comes naturally or is built into macros agents use in email and text exchanges.” - Mark Smith, Customer sentiment: What it is and why you need to measure it, Zendesk; Twitter/X: @Zendesk

21. Use sentiment analysis to inform content development. “Customer sentiment analysis helps marketing teams create content to address common customer concerns. For example, case studies can show how other, similar users complete their jobs to be done (JTBD) with your product. At the same time, product teams can act fast to fix bugs, user experience (UX) and user interface (UI) design issues, and remove barriers to conversion or adoption.” - 4 real-life examples of successful sentiment analysis to inspire you, Hotjar; Twitter/X: @hotjar

22. Use sentiment and emotion analysis to develop a data-driven marketing strategy. “When a company launches a new marketing campaign, the company can use customer sentiment analysis to perform market research and gauge the target audience’s reactions to the campaign. Then, they can tweak or rethink the marketing strategy based on their analysis. Identifying common sentiments about a product or service can be the seed for an effective marketing campaign that responds to customer sentiment in real time.

“If a campaign evokes a negative sentiment, it will be harmful to both brand reputation and the sales or growth of the product or service. This is where the advanced sophistication of a sentiment analysis tool is especially useful. These tools go beyond identifying that an ad is not performing well—they can identify specific sentiments toward a campaign and recognize the particular aspects that evoke those sentiments.

“Emotional reaction to outreach can also guide direct campaigns. Sentiment analysis can tell a company that younger people hate email and that an older demographic dislikes social media. The company can then respond by setting up an email contact for older customers and a chat option for the younger demographic.” - Hannah Clark, What Is Sentiment Analysis: A Brief Guide To “Opinion Mining”, The CX Lead; Twitter/X: @CxLead

23. Use sentiment analysis to personalize your customer interactions. “By decoding the clients’ feelings, you can personalize communication to improve the customer experience. Sentiment analysis tools leverage historical data to define buyers’ pain points. In the future, it will let you spot similar situations and reach out to prospects proactively to offer your help.

“Let’s say you run an online store that sells a wide range of products, including clothing, electronics, and home goods. You have a large customer base and want to provide a personalized experience to each person.

“Suppose someone leaves a negative review of a product they purchased, saying it was not as described and did not meet their expectations. Using sentiment analysis, you can quickly identify the negative coloring of the review and start a conversation with the person to propose a solution, such as a refund or exchange. It shows everyone that you pay attention to their feedback and care about their experience, which can help build a stronger relationship with them.

“On the other hand, if a buyer leaves a glowing review of a pair of shoes they purchased, you can recommend similar shoes or accessories they may like based on their sentiment and past purchase history.” - Kate Parish, 5 Ways to Use Sentiment Analysis to Improve Customer Experience, ShortStack; Twitter/X: @ShortStackLab

24. Sentiment analysis is a valuable tool for improving the accuracy of root cause analysis. “Root cause analysis is an important aspect of the quality assurance process for call centers, enabling you to look beyond symptoms and address the underlying causes of customer dissatisfaction. By including sentiment data in your reports, you can improve the accuracy of root cause analysis.

“Merging these functions allows you to spot patterns, trends, and correlations you may have missed. For example, you may note that customer retention rates are dropping as negative sentiment increases. By examining the negative calls that lead to churn, it might become clear that your subscription renewal process frustrates customers, driving them away.” - Shane Croghan, The 8 Best Ways to Use Call Center Sentiment Analysis, Scorebuddy; Twitter/X: @score_buddy

25. Don’t neglect the importance of a human perspective. “...there are many nuances, cultural differences, and local slang that could affect the accuracy of sentiment analysis.

“While social listening tech has evolved to give an “accurate enough” analysis of sentiment, there are scenarios that will still call for a human perspective. Especially when it comes to sentiment analysis in non-English conversations, having a human analyst to add more cultural context is important.” - Human vs. machine: Can tools provide accurate sentiment analysis?, The Social Intelligence Lab; Twitter/X: @TheSILab

Sentiment analysis is a crucial tool for understanding how your customers feel about a brand and its products or services, but choosing the right technology is key. An advanced sentiment analysis solution like CallMiner’s conversation intelligence platform analyzes 100% of customer interactions and provides detailed insights about customer sentiment and emotion. CallMiner goes beyond the limits of basic sentiment analysis to provide a complete understanding of the customer journey across every touchpoint.

Frequently asked questions

How does sentiment analysis use AI?

Sentiment analysis tools often utilize artificial intelligence (AI), particularly machine learning and NLP, to automatically analyze and interpret textual data to determine the emotional tone behind the words. This is widely used in analyzing customer feedback, social media conversations, and other forms of textual content to understand public sentiment toward products, services, or topics.

What are the four types of sentiment analysis?

The four main types of sentiment analysis are:

1. Polarity-Based Sentiment Analysis: This is the most common form, where sentiments are categorized into positive, negative, and neutral. It's primarily used to gauge overall sentiment towards a product, service, or topic based on textual data.
2. Aspect-Based Sentiment Analysis: Rather than analyzing the overall sentiment, this approach focuses on identifying sentiments related to specific aspects or features mentioned in the text. For example, in a product review, different sentiments might be expressed about the price, quality, or customer service.
3. Emotion Detection: This goes beyond simple polarity and identifies specific emotions like happiness, anger, sadness, or surprise. Emotion detection is often more nuanced and can provide deeper insights into the user's feelings and attitudes.
4. Intent Analysis: While not always categorized under sentiment analysis, intent analysis is closely related and often used in conjunction. It involves determining the intent behind a text, such as whether a customer intends to purchase a product, needs support, or is considering leaving a service. Understanding the intent can be crucial for effective customer relationship management.

What is NLP in sentiment analysis?

In sentiment analysis, NLP (Natural Language Processing) is the technology used to help computers understand, interpret, and manipulate human language. NLP breaks down language into shorter, elemental pieces, helps the machine understand relationships between these pieces, and interprets the meaning, context, and sentiment of the text.

How do you gather data for sentiment analysis?

Data for sentiment analysis can be gathered from various sources, including social media platforms, online reviews, customer feedback surveys, forums, and blogs. Tools like CallMiner can be used to collect and analyze data across all customer interactions.