Sentiment Analysis And Natural Language Processing: A Beginner’s Guide
If you’ve ever used Google Translate or let Gmail autocomplete your sentences, you’ve seen natural language processing (NLP) in action. Smart assistants, like Alexa and Siri, also depend on NLP technology, as does sentiment analysis—the process brands use to analyze social media and other data sources to measure how customers feel about them.
Sentiment analysis and natural language processing models are improving fast. The latest models have been trained on over 175 billion parameters—ten times more than previous models—and are capable of back-and-forth conversation, generating text based on prompts, translation, answering questions, and advanced sentiment analysis.
So, what does this mean for customer services and customer experience (CX) teams?
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Sentiment analysis and NLP technology have important implications—especially for understanding customer needs. Customer feedback happens in more places than ever today, from social media to customer reviews to online chat. Sentiment analysis, powered by NLP, can help make sense of it all so you can create a better experience for your customers.
In this beginner’s guide to sentiment analysis and natural language processing, I’ll walk you through the basics of sentiment analysis and NLP. I’ll explain what each is, talk through real-world use cases, and recommend sentiment analysis tools that make life easier for customer success and customer experience teams.
What Is Sentiment Analysis?
Here’s the million-dollar question for CX and customer service teams:
What are your customers thinking?
If you can answer that question, you can avoid negative feedback, gain a competitive advantage, and tailor your product to precisely fit customer needs.
That’s where sentiment analysis comes in.
Sentiment analysis uses NLP models to scan vast quantities of data, from customer reviews to support calls to tweets. Then, it assigns a score to each customer comment based on whether the sentiment is negative, neutral, or positive.
It’s not a perfect system. Even when humans categorize statements into positive, negative, or neutral, they don’t always agree. But the advantage NLP models have is that they can use a consistent text analysis framework—applying the same criteria to massive amounts of data.
Using sentiment analysis can help you:
- Measure customer satisfaction at each stage of the customer journey.
- Track brand sentiment so you can notice unhappy customers immediately.
- Compare sentiment over time to see if you need to take action.
- Conduct market research and design marketing campaigns.
3 Types Of Sentiment Analysis
Unless you’re a data scientist, you don’t need to know too much about what’s going on behind the scenes of your sentiment analysis tools.
But as you’re evaluating tools, it can help to have a basic understanding of the technology behind them.
Here are three types of sentiment analysis:
1. Knowledge-based
When you use a knowledge-based (or lexicon-based) sentiment analysis system, you’re analyzing text based on a predetermined set of rules.
Think of this as the “dictionary” approach.
Your dictionary will have a long list of words and accompanying sentiments—for example, the word “hate” might be classified as negative sentiment, “love” as positive, and “okay” as neutral. You can create these dictionaries manually, but more commonly they’re created using machine learning algorithms.
The knowledge-based approach is useful for industries that use specialized language. For example, you might use a specialized dataset of medical terms to measure customer satisfaction in a healthcare setting. You can also use this approach to carefully classify complicated languages like metaphors, slang, and sarcasm.
However, using a knowledge-based approach is usually less accurate than statistical sentiment analysis because it draws from a much smaller pool of semantic data.
2. Statistical
Imagine manually labeling hundreds or thousands of pieces of text as ‘positive,’ ‘negative,’ or ‘neutral’—and then training a model to create classifiers based on that data.
That’s how the statistical sentiment analysis method works.
The statistical approach uses machine learning models to analyze large quantities of data and identify patterns and relationships. Once the model has been trained, it can be used to analyze any given text—not just text that’s similar to its dataset. This makes it more flexible and accurate than the knowledge-based approach.
A drawback of the statistical method is that it requires a large amount of data to train. Statistical models can also struggle with languages like sarcasm and irony.
3. Hybrid
A hybrid approach combines the strengths of the statistical and knowledge-based methods.
Starting with the statistical method, you first create a flexible sentiment analysis model trained on large quantities of data. Then, you refine and improve the model using the knowledge-based method.
This gives you the flexibility and large dataset of the statistical method and the fine-tuning and industry expertise of the knowledge-based method.
Related read: Best Survey Analysis Software
What Is Natural Language Processing?
Natural language processing is a type of artificial intelligence that enables computers to understand and generate human language. NLP powers sentiment analysis. You’re likely also familiar with some of its wider practical applications like:
- Flagging emails as spam
- Chatbots and conversational AI
- Speech-to-text and text-to-speech software
- Machine translation like Google Translate
- Speech recognition, like Siri, Alexa, and other virtual assistants
But how does NLP work?
NLP starts with text data that needs to be analyzed. The text is then cleaned and formatted to make it easier to analyze. NLP algorithms are then used to find meaning in the text. These algorithms use statistical models and rule-based methods to identify patterns and connections.
Customer service AI chatbots and voicebots are among the most visible applications of NLP in the customer experience. They power a significant amount of customer interaction, using NLP techniques to understand what customers are saying, respond appropriately, and provide customer support.
Historically, though, the experience of interacting with these bots has been somewhat frustrating.
That’s changing fast as NLPs advance—especially with the advent of LLMs.
Large language models (LLMs) are deep-learning neural networks that have been trained on vast amounts of data. For example, OpenAI’s GPT-3 model was pre-trained on hundreds of billions of words from books and sources like Wikipedia.
These large datasets give LLMs a huge baseline of knowledge, the ability to generate sophisticated, human-like text, and an added level of context and nuance when analyzing sentiment.
How Does NLP Sentiment Analysis Work?
NLP sentiment analysis is a technique that identifies emotion expressed in text using models that classify words and phrases as negative, positive, or neutral.
Sentiment analysis tools review and classify large quantities of customer feedback in order to provide an overall sentiment score.
Here’s how it works:
- Training: An NLP model is trained on an existing dataset to learn how to classify sentiment.
- Input: A large amount of customer feedback, such as social media comments, emails, or product reviews, is fed into the model for opinion mining or text mining.
- Simplification: The model removes stopwords and irrelevant information, like greetings or sign-offs. It further reduces the size of the data through stemming, the process of removing the suffix of a word.
- Identification: The model scans the remaining text to look for important verbs, nouns, and other parts of speech.
- Classification: The model assigns a positive, negative, or neutral score to each word or phrase. For instance, the word "excellent" might receive a positive score, while the word "terrible" might receive a negative score.
- Scoring: Finally, the tool combines the scores to produce an overall sentiment score. This helps brands identify specific customer feedback that requires attention or action and also helps measure overall brand sentiment changes.
Here’s a simple example of how this works for brands.
Imagine seeing the following Yelp online reviews for a pizza restaurant:
- "I love Luigi’s—best pizza I’ve ever had. They treat me like family every time I show up.”
- “The pizza here is pretty good. I’ve had better pizza elsewhere, but Luigi’s is reliable.”
- “My pizza showed up cold, and the crust tasted like cardboard. Not recommended.”
A sentiment analysis tool would classify the first review as positive, the second as neutral, and the third as negative. Then, it would provide an overall customer sentiment metric.
NLP models can also detect specific emotions like joy, sadness, or anger, adding additional context to sentiment analysis.
Which NLP Model Is Best For Sentiment Analysis?
Support Vector Machines (SVM), Random Forest, and Naive Bayes are all models that can be used to analyze text for sentiment.
Support Vector Machine (SVM)
A support vector machine (SVM) is a model that helps classify data into groups. It works by creating a line, or boundary, that separates one group of data from another. Once the boundary is created, the SVM can use it to predict which group new data points belong to.
Let’s look at an example.
Let’s say you have thousands of photos of apples and oranges and want to classify each appropriately. You’d start by giving the SVM a set of labeled data, like this:
- Apples: shape, color, size
- Oranges: shape, color, size
As you start feeding new images into the SVM, it will use the labeled data to predict whether each fruit is an apple or an orange.
Support vector machines can be used in text classification, so they can be a helpful machine-learning technique for sentiment analysis. SVMs are also used for applications like image recognition and fraud detection.
Random Forest
The random forest algorithm is used for classification and regression tasks. It works by combining multiple decision trees to make more accurate predictions.
And what, exactly, is a decision tree?
Decision trees are flowcharts that help you make decisions and predictions. Answering a question at each stage of the tree leads you to more specific questions, ultimately resulting in a higher-confidence answer.
For example, let’s say you want to predict if someone will like a certain food based on their age, gender, and location. A decision tree would split people into smaller and smaller groups before predicting that a 5-year-old boy from the United States would love chicken nuggets while a 35-year-old woman from France wouldn’t.
The random forest is a powerful algorithm because it combines the results of many different decision trees. This reduces the chances for error and creates more accurate predictions.
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Naive Bayes
Naive Bayes is a simple algorithm known for its speed and efficiency. It’s called “naive” because it assumes each word occurs in a vacuum—which, of course, isn’t how things work in the real world.
For example, if Naive Bayes were being used to identify emails with malicious links, it might calculate the probability of words like “verification” or “eFax” showing up in the subject line of a malicious email. However, it wouldn’t be able to look at the overall context of the email.
In a sentiment analysis context, Naive Bayes can be useful for its speed, so it can be a good option for the rough analysis of large datasets. However, because Naive Bayes can’t understand context, it’s not as accurate as more nuanced algorithms.
Sentiment Analysis Tools With NLP
Sentiment analysis tools fall into a couple of buckets:
- Developer tools: NLP toolkits are used by developers and typically require programming skills. Toolkits give you access to pre-trained models via API, allowing you to analyze the text without training your own model. Google and Amazon both offer NLP toolkits as a cloud computing service, for example. Another option is the Natural Language Toolkit (NLTK) library, an open-source Python package. While NTLK and other open-source tools are effective at sentiment analysis, they aren’t as user-friendly as consumer-facing tools.
- B2B Software: Sentiment analysis software is designed for commercial use, with an emphasis on user interface. These tools come with comprehensive dashboards, powerful sentiment analysis capabilities, and the ability to track sentiment over time.
Unless you’re looking to develop your own sentiment analysis model, the most effective path is to use software that’s purpose-built to help brands analyze and track sentiment.
Leading sentiment analysis tools include:
- Brand24: Brand24 is a social media monitoring tool that helps companies track their online mentions in real-time. It provides sentiment analytics that classifies information into positive, neutral, and negative categories and offers a discussion volume chart to monitor social conversations. Brand24 has a simple, actionable user interface with easy access to brand mentions and relevant keywords across social media sites, news articles, blogs, videos, forums, and reviews. It offers a mentions feed, influence score, sentiment analysis, custom alerts, and data exports.
- Diffbot: Diffbot is an AI web extraction tool that extracts data from millions of URLs, analyzes it for sentiment using natural language processing, and converts it into actionable insights. It offers features like automatic website content extraction, content categorization, natural language processing, and knowledge graphs. The tool saves time searching the web for information and is highly customizable. Some technological knowledge is required to get the most out of the tool.
- Qualaroo: Qualaroo is a feedback software that allows companies to ask questions directly on their website to gain insights from customers. Qualaroo offers survey templates, sentiment analysis, keyword identification, and emotional information. The software provides real-time results from NPS and notifications via Slack. Qualaroo is a good tool to capture customer feedback to improve customer experience and grow conversions.
- NICE: For CX teams looking for an all-encompassing cloud CX platform, NICE may be the way to go. It’s a customer experience software that uses AI to build CX models based on customer interactions. Its sentiment analysis tools collect structured and unstructured information and turn it into business insights. The tool can analyze and interpret all customer interactions to gauge real-time changes to your online reputation. NICE includes conversational and AI chatbots, robotic process automation, AI routing, performance analytics, and interactive voice response.
- Listen360: Listen360 is a customer engagement tool that helps you monitor customer experiences and responses across different online channels. It tracks positive and negative feedback from social media and the web and alerts brands quickly so they can take corrective action. The tool hones in on detractors and "at-risk" clients so that you can make improvements quickly and easily. Listen360 features include text analytics, real-time dashboards, NPS analysis, and detractor alerts.
Better CX Through Sentiment Analysis
If you’ve read this far, congratulations—you’re coming away with a strong introductory knowledge of sentiment analysis and NLP. With the accuracy of these tools improving faster than ever, there’s no better time to choose a sentiment analysis tool and get started.
When you do, remember the “why” behind your efforts:
Creating a better customer experience.
By comprehensively collecting customer feedback and analyzing it for sentiment, you can get an impressively real-time look at how customers are feeling about your brand and your product. You’ll also get a deeper understanding of how to adapt your product to customer needs.
With NLP sentiment analysis, you’ve got a direct line to your customers’ thoughts—and there’s no underestimating the impact that can have on your business.
Read more about the benefits of customer sentiment analysis here, or subscribe to our newsletter to get regular CX tips sent straight to your inbox.
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