As businesses digitally transform their solutions and processes, they’re also finding it necessary to transform how they interact with—and especially understand—their customers to be successful.
But it’s more than just offering simple customer surveys. You need to be actively involved in conversations about how your customer base feels about your brand. And sentiment analysis is the key to figuring that out.
Defining sentiment analysis
Sentiment analysis, also known as opinion mining or emotion artificial intelligence, is a natural language processing (NLP) technique that determines whether a piece of content is positive, negative, or neutral. By analysing text and statistics, a sentiment analysis tool can understand what customers are saying, how they’re saying it, and what they really mean—both from an individual’s and the public’s perspective.
Under the umbrella of text mining, sentiment analysis is routinely used to determine the voice of the customer in feedback materials and channels like reviews, surveys, web articles, and social media. As language evolves, it can become increasingly challenging to understand intent through these channels and defaulting to dictionary definitions may lead to inaccurate readings.
With an algorithm-based sentiment analysis tool adjusted to your customers’ voices, you’re able to reveal what your customers want and need from your product, service, or solution and how their opinions trend or change over time.
Some types of sentiment analysis are:
- Aspect-based—determine specifically what your customers are discussing, like product prices in online reviews, as well as the sentiments of individual customers.
- Emotion detection—pinpoint emotions by associating certain words with a particular sentiment.
- Fine-grained—analyse sentiment across polarity categories (very positive, positive, neutral, negative, or very negative) to help determine customer opinions at more granular levels.
- Intent—define your customers’ intent so you can understand if they’re purchasing or researching and if you’ll need to track and target later.
How sentiment analysis is used
Traditionally, businesses relied on questionnaires and surveys to gauge customer opinion. For example, the Net Promoter Score (NPS) survey aggregated and assessed information needed to measure customers’ willingness to recommend a business. While valuable, it can severely lack the ability to provide deeper insights into customer experiences—such as when making purchases—across your digital channels.
But sentiment analysis can bridge that gap.
In monitoring, identifying, and extracting customers’ opinions and sentiments from text, sentiment analysis can help reveal the meaning behind each comment, social media like, idea, complaint, and query. And help you readily attend to your customers’ ever-evolving needs.
By analysing the collected data, you’ll get a summary of each customer’s reaction, as well as any other additional feedback that could help shape the public perception of your product or business. When this data is placed on a positive, neutral, or negative sentiment spectrum, you’re able to see what drove the customer to make that statement—revealing the opinions that describe the customer’s sentiments and feelings towards a specific topic.
These opinions are then classified as direct (“This product is the best I’ve ever used!”) or comparative (“Product A integrated better with my org than Product B.”). While these are often easy to interpret, it’s important to also note that some may need further looking at. Classifications such as implicit (“The business knows what they need to do to improve this product.”) and explicit (“Feature A is easy to use.”), as well as word sequences that are positive yet contain a negative word, can be difficult to analyse and might require some manual review or adjustments to your sentiment models.
But once these key words and phrases on how others feel about you are discovered, they can help you plan your organisation’s next move. But first, you need to understand how sentiment analysis works to benefit your business.
Understanding how sentiment analysis works
Sentiment analysis uses several technologies to distill all your customers’ words into a single, actionable item. The process of sentiment analysis follows these four steps:
Breaking down the text into components: sentences, phrases, tokens, and parts of speech.
Identifying each phrase and component.
Assigning a sentiment score to each phrase with plus or minus points.
Combining scores for a final sentiment analysis.
By remembering descriptive words and phrases to assign them a sentiment weight, you and your team can build a sentiment library. Through manual scoring, your team decides how strong or weak each word should be, and the polarity of the corresponding phrase score, noting if it is positive, negative, or neutral. Multilingual sentiment analysis engines also must maintain unique libraries for every language they support through consistent scoring, new phrases, and the removal of irrelevant terms.
Sentiment analysis can distill these approaches into three different categories:
A mix of statistics, NLP, and machine-learning algorithms to identify sentiments. The system is trained to associate inputs with corresponding outputs, that is, customer text with polarity. Machines are classified with the input data and can adapt over time once trained. This can be tested with additional data to provide better predictions.
The most straightforward sentiment analysis uses dictionaries or lexicons to explore words and phrases and determine their associated sentiments. This type of approach works well with direct and explicit opinions. While this system is fast and easy to use, it rarely considers how words are combined in a sequence. Teams need to add rules for comparative opinions as this approach can’t readily understand implicit opinions.
Combining both rule-based and automated systems means you can gain the accuracy and precision you need to truly understand your customers. This is the most powerful system as it contains the emotional information gathered from lexicons, which can be adapted over time.
How is sentiment analysis useful?
While social media only gives a glance at how people talk about your brand online, sentiment analysis provides immediate knowledge of how the public perceives both your brand and product. Many retweets on Twitter might seem positive, but if you notice the likes are drastically outweighed by the negative comments, you can conclude that’s a less-than-positive interaction.
Sentiment analysis can also enable your company to extract invaluable customer input from internal data sources. For example, by monitoring transcripts of customers’ online chats with service and support representatives, your company can be more quickly made aware of product quality, safety, and warranty issues. Other benefits of sentiment analysis include:
- Serving as a critical point in identifying emotions towards a topic so your team can apply actionable insights across several lines of business and research initiatives.
- Saving your team time and effort as the sentiment extraction process is fully automated.
- Taking advantage of adaptive learning, which enables your team to regularly optimise, troubleshoot, and refresh predictions.
- Processing huge amounts of unstructured data quickly for real-time analysis and insights.
All these benefits offer your team a comprehensive view of what customers are thinking and how to respond accordingly. From these insights, you can guide internal teams like customer service to help enhance the user experience, or marketing and customer-facing teams to engage customer segments based on sentiment with targeted sales, marketing, and support efforts.
Examples of sentiment analysis
The best part is that sentiment analysis doesn’t work only for a single team. Every team can use this data to plan accordingly for everything from marketing campaigns to pricing strategies to order fulfilment or customer support. As different teams learn more about how customers feel about the product, brand, and business, they can use their knowledge to determine responses and optimise business operations. They may also reassess the goals of both the business and their customer, and define which actions to take to reach that goal.
Some examples of how teams use sentiment analysis include:
- Social and brand monitoring. Analysing real-time customer interactions and comments on your social channels about your brand, product, and business can offer insights into how your customers feel about all three. Companies can also use sentiment analysis of previous products as a measure for launching new products, advertising campaigns, or breaking news about your business.
- Customer service. Your customer service team probably automatically sorts customer issues into urgent and not urgent. Sentiment analysis adds another layer by analysing the language and severity of the problem in chat or email, spotlighting particularly frustrated customers for faster mediation.
- Customer feedback. In line with social monitoring, you hear directly from the customer how negatively or positively they perceive a product or brand to be. Tracking keywords related to direct customer feedback shared on social media profiles, during online chats with your teams, or through other touchpoints provides an overall measurement of the success of your product, campaign, or solution.
- Crisis prevention. To monitor media publishing, sentiment analysis tools can collect mentions of predefined keywords in real time. Your public relations or customer success teams can use this information to inform their responses to negative posts, possibly shortening—or even averting—a social media crisis before it can pick up speed.
- Market research. It’s not just enough to know how your customers feel; you need to know why. Understanding why, or why not, customers respond the way you intended is key to planning your next move—whether through marketing, sales, or direct and personalised service responses.
Having a tool that can understand complex human emotions is critical to receiving the feedback you need from your customer base. In the past, sentiment analysis required expertise in several technologies, but today, several software tools enable sentiment analysis with little to no knowledge.
Finding the right sentiment analysis tool for your business
Choosing a customer data platform (CDP) with an integrated, intelligent sentiment analysis tool should be a top priority for your business. To create successful omnichannel customer experiences, your team, as well as your organisation, needs a CDP equipped with all the capabilities needed to generate holistic, real-time customer profiles. This includes a sentiment analysis tool that can contribute new insights for optimising customer relationship management and other data you’ve collected.
Look for a CDP that uses NLP models to accurately and efficiently analyse customer opinions and emotions. Trained on a variety of data from public sources, the models should be able to generate customer sentiment scores and identify applicable business areas for targeted improvements.