Do you know what’s going on in your customer heads? 73% of your customers expect you to understand their needs and expectations, and 63% expect you to anticipate their needs. Most companies already collect large amounts of customer data, so how do you process that information to truly understand what your customers need?
Sentiment analysis helps you better understand the voice of your customer to get insight into their needs and expectations. With sentiment analysis, you summarize customer feedback data from one or more sources into positive, neutral, or negative customer sentiments (or feelings). Accurate sentiment analysis can help you better meet the needs of your growing community by understanding who’s unhappy, which issue makes them unhappy and needs addressing, and who’s happy and likely to become long-time or repeat customers in your business.
In this guide, we’ll answer the question, “what is sentiment analysis?” and uncover how your business can use it to build better business systems, offerings, and overall customer experience. We’ll discuss the following:
- What is sentiment analysis?
- What is Natural Language Processing (NLP)?
- Types of sentiment analysis
- Why sentiment analysis is important for businesses
- Benefits and challenges of sentiment analysis
- How to determine customer sentiment
- How to develop your own sentiment analysis system
- How to use machine learning for sentiment analysis
- What are sentiment analysis algorithms and tools?
- Using Idiomatic for comprehensive customer sentiment analysis
What is sentiment analysis?
Sentiment analysis helps organizations with brand monitoring to determine if feedback or customer actions are overly positive, negative, or neutral. An algorithm will assign a sentiment score, usually either positive or negative, to every interaction, conversation, or piece of feedback. Oftentimes, sentiment analysis can be more detailed than just “positive” or “negative” and include various levels of positive or negative feedback (very negative, negative, positive, very positive).
Your sentiment score is derived from various sources, including:
- Feedback forms and surveys
- Support tickets
- Chatbot texts
- Social media accounts
When you analyze customer sentiment, you can learn where customers are generally satisfied or unsatisfied with your brand, service, or product. When you use the insights from sentiment analysis, you can make changes to your business operations, processes, products, or customer services to increase customer satisfaction.
Next, let’s look at what each sentiment type really means for your business.
When you perform sentiment analysis, you hope for a majority of positive sentiments. This means the data you’ve collected from your customers indicated mostly positive or delighted customers.
When you receive overwhelmingly negative feedback, this will translate into a negative sentiment. Receiving a negative sentiment isn’t necessarily a bad thing as, with a bit of in-depth research into the causes of the negative opinions, it can help inform business decisions that can help improve the customer experience.
Your sentiment analysis system may also classify responses as inconclusively negative or positive. These are neutral sentiments. Neutral sentiments are still beneficial results because there’s still significant room to grow if your business uses this information to make changes to satisfy customers. It also means, however, that inaction could result in leaning toward the negative end of the sentiment scale.
What is Natural Language Processing (NLP)?
Natural Language Processing (NLP) is a field of computer science that helps give artificial intelligence (AI) the tools to understand the meaning or intent behind certain words. It helps us “read between the lines” of customer data.
Here are two examples of how Natural Language Processing can help translate the sentiment in user data:
- A customer mentions on social media that they struggle to update their profile picture on your SaaS platform. The NLP in your sentiment analysis system will likely determine this as a negative sentiment, indicating customer struggle or dissatisfaction.
- Natural Language Processing can determine that the phrase, “if only it were that easy,” is likely sarcasm, while less sophisticated analysis might see the word “easy” and deduce this is positive sentiment feedback. Natural Language Processing, however, can use surrounding context and data to determine that this is an unhappy customer poking fun at this feature.
While general NLP models can surface high-level sentiment themes, they’ll still require a manual deep-dive to address the specific root of the problem and action on it. For example, NLP might tell you there’s been a spike in payment issues, but you’ll need to go searching for the reason why.
To be able to take action quickly, you’ll look for a tool like Idiomatic that customizes labels per channel, per customer. Instead of using general NLP, Idiomatic creates their own training data, then uses supervised machine learning to train custom models to label customer feedback specifically, identifying trends you didn’t even think to look for (or have tags for).
Using contextual machine learning, you’ll then get specific insights to drive action and improve the customer experience. Taking our example before, Idiomatic’s platform wouldn’t just tell you there’s an issue with payments; it would tell you there’s been a 15% increase in payments failing due to incorrect bank details so you know exactly what needs to be fixed.
Types of sentiment analysis
Sentiment analysis encompasses many different types, including fine-grained, emotion-based, intent-based, or aspect-based:
|Fine-grained analysis||Emotion-based analysis||Intent-based analysis||Aspect-based analysis|
|Definition||Fine-grained sentiment analysis uses rating scales to measure specific and extremely positive and negative sentiment levels.||Emotion-based analysis focused on feelings and emotions rather than categorizing responses as either positive or negative.||Intent-based analysis uses data to deduce if the feedback is an opinion, question, suggestion, complaint, news articles, or appreciation.||Aspect-based sentiment analysis aims to separate specific features, categories, or topics, to determine the sentiment for each area individually, rather than a broader sentiment.|
|Example||Questions that rank satisfaction using a rating scale. For example, a 1-5 rating scale:|
1 = Very negative
2 = Negative
3 = Neutral
4 = Positive
5 = Very positive
|Examples include analyzing responses to determine a specific emotion or feeling, including frustration, anger, sadness, or joy.||For example, you may see social media comments from many people expressing frustration (negative sentiment) when upgrading their account. Intent based analysis would determine that this is a complaint. If CNN reports on the issue, it would be a news article.||For example, determining that if someone leaves an overall positive comment but mentions that the settings menu is hard to find, it’s a negative sentiment about that specific feature, not the whole product or brand as a whole.|
Why sentiment analysis is important for businesses
No matter your industry or niche, your business’s purpose is to make customers happy and to meet their needs with your offerings. Knowing your sentiment score is important to help determine if customers are generally satisfied or unhappy with your brand. You can use this data to gather more detailed information regarding customer satisfaction with specific details of your offerings, such as platform usability, product features, or customer service.
You can use sentiment analysis to:
- Measure brand awareness and popularity
- Track user acceptance of new products or features
- Measure campaign effectiveness
- Conduct a social media analysis
- Do market research
- Better understanding of the customer service experience
- Understand the Voice of the Customer
You can also use a sentiment analysis tool to evaluate your data and get information about customers with negative sentiments in real time. With this, you can develop a process to reach out to them immediately to help solve their problem, whether via DM to their social media post or by contacting the customer by email.
Understand your competitors better
Sentiment analysis is an excellent tool for understanding your customers and comparing them to your competitor’s customers. You can opinion-mine publicly available data on your competitor’s brand and customers to determine customer sentiment for any feature you wish to compare.
For example, you can perform sentiment analysis on social media platforms to see what people say about your competitor. You compare this data to what people say about your business to learn more about what aspects your customers value about each brand. You can then use this to inform business decisions to beat the competition and increase your market share of happy customers.
Benefits and challenges of sentiment analysis
While sentiment analysis can give your business valuable insights into your customers, it’s not without flaws. Here are some common sentiment analysis challenges and benefits:
- Easy to digest feedback summaries: Sentiment analysis boils down to only three responses to any given question: Positive, Neutral, or Negative sentiment. It provides a high-level current state for those who need it and can go deeper to give more details about the specific feedback that contributed to that sentiment.
- Summarize substantial amounts of data into one response: With advanced machine learning or AI sentiment analysis tools, you can take customer feedback data from multiple sources (i.e., social media posts, help desk tickets, and emails) to get the overall sentiment regarding your brand or specific aspect.
- Helps inform a data-based marketing strategy: Understanding customer sentiment helps you better understand your customer’s purchasing behavior based on measurable data. You can use this measurable data to look at trends over time and compare them to your competitors.
- Helps inform brand perception: Knowing what people think about your brand is important for all aspects of business, especially sales and marketing. Sentiment data can tell you where to focus your business efforts to increase customer satisfaction and encourage brand loyalty, including improving your customer lifetime value.
- Machine learning can provide real-time sentiment analysis: With sentiment analysis technologies running on your live data, you get real-time access to customer sentiment based on 100% of online conversations. If you were to wait to get customer sentiment from a CSAT survey responses, for example, you’d wait longer for the results and only collect data from a small subset of customers who actually participate in your survey.
- Sarcasm or irony: Human and AI analysts may not notice or understand sarcasm.
- Double negatives: Some basic sentiment analysis systems may need help understanding the true meaning behind double negatives.
- Multiple-sentiment responses: If a sentence includes two different sentiments, some algorithms work to narrow that into one sentiment.
- Tone: Some AI struggle to understand the emotional tone in written language. We know how you speak can affect the meaning of the words, but some analysis software needs help to interpret emotional tone and meaning accurately.
How to determine customer sentiment
Where do you get sentiment analysis datasets to use with your sentiment analysis models? It usually comes from three areas: customer feedback, customer usage or interaction data, and opinion mining:
Customer feedback is a useful source of data for sentiment analysis. We like it because it provides feedback using the Voice of the Customer, and leaves less room for misinterpretation by data scientists. Customer feedback can come from many places, including:
- Help desk (Customer support tickets from form, email, phone, or chat)
- Surveys (NPS, CSAT, CES, feedback)
- App reviews
- Social media conversations
- Forums and communities
- Product reviews
Customer usage or interaction data
Often your business keeps additional behavioral data on its customers, like browsing data, app usage, and purchase history and frequency. A customer sentiment analysis tool can take this data to better understand and substantiate sentiment claims based on other data sources.
Opinion mining sources
Opinion mining searches for publicly available sources that mention your organization. The most common are social media conversations, online review sites, or blogs, and news articles that review or talk about your company or offerings. Using these sources of information, your AI can look for positive and negative words used in the context of your brand to determine sentiment.
How to develop your own sentiment analysis system
Once you’ve collected feedback data from your customers that you want to analyze, you can develop your own sentiment analysis process or use machine learning and software to get your results.
If you choose to develop your own sentiment analysis model, here are the steps:
Step 1: Choose a machine learning option
You’ll likely want to use AI or machine learning algorithms to review and analyze your data rather than doing it all manually. Research the algorithms and programming languages that best meet your goals and analytics budget.
You might consider using the following sentiment analysis tools:
|For Python||For Java|
|Natural Language Toolkit (NLTK)||OpenNPL|
Step 2: Build your model
If you have a good data analytics programmer on your team, they can write the algorithms for you. You also have the option to use or start with open source or purchase off-the-shelf algorithms.
Step 3: Train your model
Likely your first algorithm won’t work perfectly. Input test data into the system so your algorithm can begin learning how to label and analyze the data. This may involve some manual tagging by data scientists on your team, which is time-consuming. It may also necessitate creating a user-friendly interface for non-programmer team members to assist with data uploading and tagging without going into the code.
Step 4: Onboarding
Once you have a good model, begin onboarding team members using the tool. You can also manually program automatic notifications (via email or SMS) to alert specific team members if certain conditions are met. This is helpful when there is a sudden influx of negative sentiment regarding a particular category.
How to use machine learning for sentiment analysis
You can develop your own sentiment analysis solution where data is analyzed manually by your team members. However, this is not efficient. It also doesn’t guarantee a non-biased interpretation or the level of detail you need.
Sentiment analysis is best done using machine learning platforms. Here’s the basic process to develop your own sentiment analysis solution using an AI platform (like Idiomatic):
Look across your company for all the customer feedback data sources to integrate into your analysis platform. This includes structured data (quantitative data like ranking questions or yes/no questions) or unstructured data (like survey comments and feedback forms).
Idiomatic includes many integrations with popular third-party data sources (including Zendesk), making uploading your data for sentiment analysis easy.
Step 2: Do a thematic analysis of unstructured data
There are two ways to do thematic analysis. The first way is by manually classifying text using keywords to categorize feedback and include it in rule-based systems.For example, you can categorize emails by the topic or category they reference, such as those that refer to customer support teams, product feedback, or business systems feedback.
The second (and arguably the correct way) is by using AI like Idiomatic’s platform, which classifies millions of customer comments within minutes using labels calibrated to specific channels of your customer feedback for the most accurate picture possible.
Step 3: Run a sentiment analysis
Now you can run a sentiment analysis on any area of your business you need or set it up for real-time notifications and monitoring (like with Idiomatic). Use a sentiment analysis tool with a dashboard to display your sentiment results, highlighting keywords or topics that require your attention, usually due to negative sentiment. This helps you identify core issues immediately so they can be solved to increase customer satisfaction and sentiment with that aspect of your business.
Look at your sentiment scores for both positive and negative sentiments so you know where you’re doing well, and which areas may need improvement. Don’t forget to look at neutral sentiment, too, as it may need to be addressed before it creates a negative customer experience.
Step 4: Use sentiment analysis and other metrics for a deeper insights
Combine your results from sentiment analysis with other metrics to better understand your customer. Sentiment scores can help explain less detailed results from a Net Promoter Score survey or why customer churn consistently increases at a specific point in the customer journey.
If you notice a high customer churn, look at customer sentiment score related to that stage in their customer journey. You may discover a sudden dissatisfaction with an aspect of your business.
For example: If you notice many customers don’t renew after their first year, you might look at the sentiment analysis and discover that people have a negative sentiment regarding your self-serve customer profile system (which includes the options for renewal). Upon further evaluation, you might determine that customers can’t easily see the renewal button in their profile, so they give up without renewing.
Based on this data, make the renewal button larger and in the header or every page when the user is logged in or send an automated email one month before their subscription ends with a direct link to renew their account.
Step 5: Human Analysis
The algorithm has already done the hard work. Now you can have real people on your data analytics team review the data and tweak it if necessary. They can update the algorithm if they notice obvious misinterpretations of the data. For example, a machine learning model might see the term “dispute” as a negative sentiment for most industries, but if you’re in the banking industry you’d want this term interpreted as neutral.
This team can also pass sentiment data on specific business areas to other departments for deeper manual analysis to inform business changes if needed to increase customer satisfaction.
What are sentiment analysis algorithms and tools?
To make sentiment analysis work, you need a way to collect and analyze your data. This is where sentiment analysis tools become helpful. Some analysis tools specialize in a specific category of data (such as social media listening), not providing the full-rounded analysis you need. If you want a full-picture analysis of your brand, look for a tool that can pull data from various sources (not just one platform or medium) to provide you with either channel-level sentiments (i.e.: Customers on Twitter have a generally positive sentiment regarding our subscription model), or higher level sentiments (i.e.: Customers in North America are generally happy with our brand).
Some common sentiment analysis tools include:
Idiomatic’s AI-driven sentiment analysis software helps you classify and analyze millions of customer comments from multiple sources in minutes. Customers like FabFitFun, Instacart, and Pinterest have all used Idiomatic to analyze large amounts of feedback data and get actionable, meaningful insights to boost customer satisfaction and positive sentiment score.
Idiomatic uses user issue analysis with sentiment analysis to help you see what issues are causing users to have a negative experience and alert you to real-time changes in sentiment. When this voice of customer software detects a change in customer sentiment, you get real-time alerts so you can take action immediately, whether fixing a minor code bug or contacting a customer directly to solve their problem.
Talkwalker focuses on social media monitoring and sentiment analysis. It analyzes comments and engagement on social media to help determine how happy your customers are. It’s excellent at analyzing social media but doesn’t integrate other data sources.
Critical Mention focuses on analyzing news, publications, and TV for mentions of your business. Again, this tool concentrates on one data source type but does that with detail.
Hubspot’s Service Hub
Hubspot breaks down qualitative survey data into positive and negative sentiments for summative analysis. It integrates directly with their other suite of marketing and sales tools but comes with an additional monthly fee of up to $1,200 per month.
This platform uses multilingual sentiment analysis using over 30 different languages. If your business is international with customers who natively speak languages other than English, this tool can be helpful.
Using Idiomatic for comprehensive customer sentiment analysis
Idiomatic is the next frontier of sentiment analysis. Its AI-driven sentiment analysis software helps you classify and analyze millions of customer comments from multiple sources. It provides a crystal clear, data-driven view of customer sentiment in real time. You can use this data to eliminate pain points to create loyal, lifelong customers.
Sentiment analysis helps you discover the “why” behind your customer feedback. With Idiomatic, you can save time and money compared to dedicating manual resources to analyze your data or create your own sentiment analysis algorithm and platform from scratch.
Request a demo of Idiomatic to inform the right business decisions and increase your customer loyalty and satisfaction.