How to use sentiment analysis to analyze customer opinion

A great relationship depends on how sincerely you understand the other person. And this hinges on truly listening and paying close attention to what they say and how they feel. The same principle applies to brands and consumers.

To stay connected, brands must consistently measure and analyze market sentiment, asking critical questions like, "How do customers feel about my brand?", "Are my customers likely to switch to a competitor?", or "What content resonates most with my audience?"

Luckily, AI techniques like sentiment analysis do this automatically. Sentiment analysis algorithms scan millions of comments within seconds to surface deep insights showing the roots of an issue and quantifying customer sentiment. In other words, they take the guesswork out of the equation.

In a world where customer loyalty is fickle and competition gains ground quickly, sentiment analysis provides an efficient way to extract important insights from social listening and online reviews. This valuable information helps you build meaningful relationships with your audience that drive long-lasting brand loyalty and increase market share.

In this article, we’ll explore the different types of sentiment analysis, why it’s essential for businesses and how brands like you can use it to enhance your brand strategy.

What is sentiment analysis?

Sentiment analysis or opinion mining is an AI technique used to understand if the sentiment expressed in data is positive, negative or neutral. These algorithms work together with other AI tasks like named entity recognition (NER), natural language processing (NLP) and machine learning (ML) to measure sentiment in data quickly and efficiently.

Brands use sentiment analysis tools in AI marketing to surface critical information in voice of the customer (VoC) data from various sources to track brand health, improve products and enhance customer engagement and retention. These sources include social listening data, reviews, surveys, customer care interactions and direct messages.

Tools like Sprout Social have sentiment mining as a built-in feature of AI automation so you can proactively harness social insights to monitor brand health, improve efficiency and drive business impact.

Why is sentiment analysis important?

A sentiment analysis tool enables brands to easily examine conversations and interactions, so they can quickly identify customer preferences at each stage of the customer journey.

Here are several ways brands can use sentiment analysis.

Gain timely insights at speed and scale

Sentiment analysis tools process hundreds of comments in minutes and handle millions of API calls daily. This enables brands to get timely customer insights to monitor brand performance to grow and engage audiences.

Get more accurate sentiment scores

Machine learning algorithms calculate sentiment from scattered customer feedback data at scale and automatically measure scores more accurately than if done manually. This also reduces the scope of biases through human emotion that can creep in. For example, a store manager may ignore a negative comment because it's from a former employee.

Identify sentiment in recurring topics and themes

See sentiment in recurring topics around your business on social media to know if they’re positive or negative. Word clouds can also give you a quick view of the important themes and topics in customer discussions.

Pairing these trending topics with sentiment helps you understand customer opinions about your brand more holistically. Plus, it gives you actionable insights to improve your products, offerings and customer care.

Analyze multilingual VoC data

Multilingual data is common in social listening. Sentiment analysis tools enable brands to understand culturally and ethnically diverse customers for a holistic understanding of brand sentiment.

Improve marketing campaigns and brand health

Brands can accurately measure key performance indicators (KPIs) to identify which parts of their strategy are effective and which need improvement. This is crucial for evaluating the success of marketing campaigns and making real-time adjustments to ensure better results. Also, manage brand perception and draw brand insights by monitoring sentiment in social mentions and customer reviews.

How does sentiment analysis work?

Sentiment analysis is a complex process, so we’ve broken it down into three essential steps.

Step 1: Data collection

It’s important to choose the source of your VoC data as it will affect the accuracy of your sentiment analysis insights. Social media networks and online platforms your target audience uses most frequently are often the best sources. Ensure the sentiment analysis tool you use integrates with these networks so you get the most accurate insights about customer opinions about your brand and industry.

Steps 2 & 3: Data processing and Sentiment mining

Once the tool collects the data, it’s automatically cleaned to make it readable by text analysis algorithms. This is done by removing special characters and redundant information and then tokenizing it.

Named entity recognition (NER) then identifies brand mentions, locations, currencies and other information relevant to the insights you want to gather. After this, semantic search algorithms enable the tool to understand comments contextually and help avoid duplicate entries. This leads to more accurate sentiment scores.

NLP sentiment analysis identifies terms in the data that denote emotions, while part-of-speech (PoS) taggers ensure non-English data is natively analyzed for multilingual sentiment analysis. All these steps help with noise filtration so only relevant information is collected based on your goals.

You can see this action in Sprout’s Query Builder when you select the Exclude Noise option. This capability ensures that all comments with a unique set of keywords are excluded. You can also create rulesets based on themes such as customer service or product reviews. 

Finally, the sentiment polarity of each clustered topic and aspect is calculated to provide the overall sentiment score.

Step 4: Data visualization

Sentiment trends and data patterns are visualized into charts and graphs, which can also be shared with other teams such as marketing and customer service. These include metrics like:

• Overall sentiment with classification
• Changes in sentiment over time
• Related keywords and hashtags
• Sentiment overview on specific social media channels
• Sentiment overview by data source (text, tag, media type, etc)
• Sentiment overview by country, region and other demographics

What are the types of sentiment analysis?

The three most common types of sentiment analysis are Document-based, Topic-based and Aspect-based sentiment analysis (ABSA)—all of which depend on the degree of the algorithm’s ability to detect sentiment polarity expressed in a given text. Brands can choose different types of sentiment analysis based on their specific needs.

Document-based sentiment analysis

Document-based sentiment analysis evaluates the overall sentiment of an entire document. It assesses whether the emotions in the data convey a positive, negative or neutral tone. This type is usually used to understand the general sentiment in individual comments, long-form articles and reports.

Topic-based sentiment analysis

Topic-level sentiment analysis is used to understand complex or scattered data. It breaks down sentences and paragraphs to identify recurring words and phrases and classifies them into topics, which it measures individually for sentiment.

When used to analyze customer comments on social media or review platforms, topic analysis helps you better understand the key points mentioned in the text, which you can then track.

Aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is the most sophisticated of the three. It uses semantic classification to understand comments that don't contain high-intensity positive words such as “love” or “hate”, to calculate sentiment. This also helps it understand the context necessary for accurate sentiment scores.

For example, this X post does not have words that denote high-intensity sentiment. Instead, it has a conversational tone and says that the cute notes and autumn stickers make the customer return to the store. Advanced sentiment mining through ABSA can break down complex sentences and identify aspects such as cutesy, cup and notes, and understand words such as "return", to identify the general sentiment (positive) and calculate sentiment polarity.

This advanced capability makes ABSA the preferred method to get the most accurate sentiment results, so brands can make informed decisions.

Business applications of sentiment analysis

Here are some important ways sentiment analysis helps brands monitor shifts in market and consumer trends and stay ahead of competitors.

Monitor industry trends

Brands have to constantly keep up with changing industry trends and customer preferences. Sentiment analysis helps you monitor social and online chatter simultaneously from multiple platforms to track emerging trends in consumer feelings across timelines. This enables you to be ahead of the curve and proactively improve your products and services for maximum return on investment (ROI).

Conduct competitive analysis

Sentiment analysis helps you examine conversations about competing brands to track their strengths and weaknesses and inform your business strategy. With Sprout, see a side-by-side competitor comparison of key performance metrics across social such as share of voice, engagement, impressions and user sentiment all in one place.

For even more detail, competitive monitoring is an effective way to gather and understand how your competition performs in terms of sentiment and a number of other metrics compared to your brand.

Create engaging content

Create compelling content that resonates with your customers and outperforms your competitors. Analyze sentiment in social listening and review hot topics to build closer relationships with your audience through personalized content based on what they’re talking about.

NBA team, Atlanta Hawks, uses sentiment analysis as part of their strategy to grow their brand by creating engaging content that resonates with their fans. The brand uses social listening to analyze its social data and identify overall audience preferences and recurring themes. By keeping track of audiences' evolving tastes, sentiment toward the brand and using actionable insights from Sprout, the team increased their video views by 127.1% and grew their Facebook audience by 170.1% within three months.

Nurture employee advocacy

Maximize brand amplification and level up employee advocacy by automating the delivery of impactful content to employees, like Medallia did using Sprout. Using sentiment analysis to know what inspires audiences the most helps you build a holistic strategy to reach a bigger audience with automated content distribution.

Improve brand reputation and increase ROI

Positive brand mentions like customer recommendations or praise are important to track so you can reshare positive mentions to amplify your brand and build social proof. Similarly, alerts based on keywords or for a particular brand campaign help you measure market sentiment and take necessary action based on your insights.

Uncovering these insights also helps brands optimize business operations and improve ROI, as building materials pioneer James Hardie® did using sentiment analysis. The brand uses sentiment insights from social listening to improve brand health and shares insights with its sales and product teams to drive strategic improvements.

Triage inbound messages

Quickly identify incoming messages from frustrated or unhappy audience members so that customer care teams can prioritize accordingly and act swiftly. Sprout users on the Advanced Plan can tap into AI-powered sentiment in the Smart Inbox and Reviews Feed. Posts will automatically be assigned a positive, neutral, negative or unclassified value, so it's easy to isolate messages and assign Automated Rules according to sentiment.

Challenges in sentiment analysis

Sentiment analysis relies on how well a machine learning model is trained to analyze data. But even with advanced versions, certain issues can lead to inaccurate results. These include:

• Sentiment polarity: Sentiment analysis can easily identify keywords that denote emotions, such as “love”, “hate” or “best”, which have high positive (+1) and negative (-1) polarity scores. The challenge arises when the text has phrases such as “not so bad”, which the model doesn't recognize. This can result in an incorrect sentiment score.
• Sarcasm: People use irony and sarcasm in casual conversations and memes on social media. Machine learning doesn't recognize back-handed compliments as negative and can misconstrue a sarcastic remark as positive.
• Emojis: Sentiment analysis primarily analyzes text to identify emotions, but comments on social media can be inundated with emojis. To overcome this problem, you need tools such as Sprout that identify emojis in text data and consider while measuring brand or product sentiment.

• Figures of speech: Machine learning programs don’t understand figures of speech such as idioms and metaphors. For example, an idiom like “under the weather” will make no contextual sense to a sentiment analysis tool. It will disregard the text and this decreases the accuracy of results.
• Negations: A sentiment mining tool cannot recognize double negatives in sentences such as, “I can’t not have my Starbucks!”. In such cases, the tool will either disregard the comment or classify it as negative, thus skewing the sentiment score.
• Comparative sentences: Comparative sentences are tricky because they don’t specify whether the customer is happy or unhappy unless specified. For example, the comment “The Galaxy S20 is larger than the Apple iPhone 12”, can be construed as positive or negative based on the customer’s preference.
• Multilingual sentiment analysis: When comments are in multiple languages, it compounds the challenges of a sentiment analysis algorithm. Luckily, this problem is solved when the tool has built-in native multilingual sentiment mining, like Sprout.

How is sentiment score calculated?

Sentiment scores are measured in polarities, where -1 is negative, 0 is neutral and 1 is positive. These can be calculated in three ways:

Word count method

In this method, sentiment scores are calculated by reducing the number of negative occurrences from the positive ones. For example, 1 - 2 = -1. Thus, the sentiment measured is negative. This method is used for simple texts.

Sentence length method

The score is calculated based on the length of the sentence, where the total number of positive occurrences is subtracted from the negative ones and then divided by the total number of words. For example, 1-2/42 = -0.0238095. This method is usually used for complex sentences and longer comments.

Word count ratio method

This is the most popular way to calculate sentiment scores. The total number of positive occurrences in the text is divided by the total number of negative ones and added by one. For example, 1/ 2+1 = 0.33333.

Sentiment scores are calculated in percentiles to give you the overall score of your brand, product or customer experience data.

Learn more about the advancements made in calculating sentiment scores.

What are the sources of sentiment analysis data?

Sources of sentiment analysis data come from various platforms where people express their opinions, like social media, customer reviews and surveys. These data sources provide a wealth of unstructured text that can be analyzed to identify customer sentiment, brand perception and rising market trends.

• Social media: Social media is a comprehensive bank of unfiltered thoughts and opinions that people express on various topics. It’s one of the best sources to get deeper insights into how your customers feel about your brand, content or customer service. With Sprout, easily measure this sentiment from a range of networks like X (formerly known as Twitter), Facebook and Instagram—all in one centralized platform.
• Review platforms: Business review platforms help you measure brand health. Sprout enables you to analyze several review platforms like Google My Business, Yelp, Tripadvisor and Glassdoor to collect actionable data.

• News: Tap into news articles to track sentiment for your brand and competitors.
• Podcasts: Podcasts are a popular source for collecting sentiment insights on trending topics that may indicate emerging industry trends.
• Surveys: Analyze survey responses for sentiment to get holistic brand insights that go beyond net promoter scores (NPS) or star rating systems.
• Customer service interactions: Customer care emails, call logs and tickets are a key source to track and address the common themes in customer complaints.
• Other text sources: Sentiment analysis is possible on any text-based data. This includes electronic health reports such as healthcare data and research papers; public information, as in government websites and platforms‌ and even gaming sites like Twitch.

Unlock customer sentiments for better brand decisions

It’s important to continuously monitor and measure customer sentiment to enhance your brand perception and deepen customer loyalty. It’s also the most reliable way to ensure long-lasting customer relationships for sustainable revenue generation.

AI-powered sentiment analysis helps you spot emotions hidden in social listening data keywords easily and quickly. Dig through hashtags across platforms like Instagram, X, Reddit and YouTube to measure customer satisfaction to enhance brand performance. Also, surface rising trends and topics among your target audience to quickly tailor your business for success.

Find out how sentiment analysis can help your brand. Request a free Sprout trial today.

Sentiment analysis FAQs

What are the three types of sentiment analysis?

The three most common methods—document-based, topic-based and aspect-based sentiment analysis (ABSA)—all of which depend on the degree of the algorithm’s ability to detect sentiment polarity expressed in a given text. Brands can choose different types of sentiment analysis based on their specific needs.

What are the four main steps of sentiment analysis?

The four steps of sentiment analysis are data collection, data processing, sentiment mining and data visualization.