Knowing how your customers feel about your brand can be a superpower. And, since sentiment is often shared through online platforms like ecommerce sites, social media, and digital accounts, you can use those channels to access a deeper, almost intuitive understanding of customer desires and behaviors.
Sentiment analysis lets you understand how your customers really feel about your brand, including their expectations, what they love, and their reasons for frequenting your business. In other words, sentiment analysis turns unstructured data into meaningful insights around positive, negative, or neutral customer emotions.
Here’s how sentiment analysis works and how to use it to learn about your customer’s needs and expectations, and to improve business performance.
What is sentiment analysis?
Sentiment analysis, or opinion mining, analyzes qualitative customer feedback (often written language) to determine whether it contains positive, negative, or neutral emotions about a given subject.
Sentiment analysis involves gathering feedback data from your company’s solicited and unsolicited feedback channels, including survey responses, feedback forms, social media comments, emails, and chatbot conversations.
You then use sentiment analysis tools to determine how customers feel about your products or services, customer service, and advertisements, for example.
How does sentiment analysis work?
Sentiment analysis breaks down text into smaller words or phrases and then typically uses technology—such as natural language processing (NLP) and machine learning algorithms—to analyze and interpret the emotions and opinions expressed in the text and assign a sentiment score to each component. Sentiments are then aggregated to determine the overall sentiment of a brand, product, or campaign.
Sentiment analysis approaches
There are three main ways to perform sentiment analysis:
1. Rule-based
Rule-based sentiment analysis is human-driven, using NLP techniques to develop a set of rules to determine a text’s sentiment. For example, a marketer might create a rule: “Comments that include ‘disappointed’ are classified as negative sentiment.”
Rules are established on a comment level with individual words given a positive or negative score. If the total number of positive words exceeds negative words, the text might be given a positive sentiment and vice versa. If there’s a tie, the text is perhaps given a neutral sentiment.
Rule-based systems are simple and easy to program but require fine-tuning and maintenance. They also don’t consider context, which can cause analysis errors. For example, “I’m SO happy I had to wait an hour to be seated” may be classified as positive, when it’s negative due to the sarcastic context.
2. Machine learning
A machine learning sentiment analysis system uses more robust data models to analyze text and return a positive, negative, or neutral sentiment. Instead of prescriptive, marketer-assigned rules about which words are positive or negative, machine learning applies NLP technology to infer whether a comment is positive or negative.
For example, take: “You’ll never be disappointed by ordering this product!” A rule-based system that labels “disappointed” as negative would likely classify this as a negative comment, whereas a machine learning approach would be more likely to infer that it’s a positive comment.
There are many NLP algorithms for sentiment analysis. One common and effective type of sentiment classification algorithm is support vector machines. If your company doesn’t have the budget or team to set up your own sentiment analysis solution, third-party tools like Idiomatic provide pre-trained models you can tweak to match your data.
3. Hybrid
Hybrid approaches combine rule-based and machine-learning techniques and usually result in more accurate sentiment analysis. For example, a brand could train an algorithm on a set of rules and customer reviews, updating the algorithm until it catches nuances specific to the brand or industry.
Continuous updates ensure the hybrid model improves over time, enhancing its ability to accurately reflect customer opinions.
Types of sentiment analysis
There are four important types of sentiment analysis:
1. Fine-grained
Fine-grained sentiment analysis uses a rating scale to categorize the text into levels of emotion. For example, when analyzing reviews and ratings, a 1–5 rating scale could translate to: very positive, positive, neutral, negative, and very negative.
2. Aspect-based
Aspect-based sentiment analysis breaks down text according to individual aspects, features, or entities mentioned, rather than giving the whole text a sentiment score. For example, in the review “The lipstick didn’t match the color online,” an aspect-based sentiment analysis model would identify a negative sentiment about the color of the product specifically.
3. Emotion-based
Emotion-based sentiment analysis goes beyond positive or negative emotions, interpreting emotions like anger, joy, sadness, etc. Machine and deep learning algorithms usually use lexicons (a list of words or phrases) to detect emotions.
However, this can sometimes result in errors, as some words with a negative connotation can be used in a positive context like, “The print on the sweater is sick.”
4. Intent-based
Intent-based sentiment analysis takes into account a text’s sentiment as well as the underlying purpose, goal, and motivation. For example, for the review, "The service was slow, and the food was cold,” the intent would be criticism or a complaint.
How to use sentiment analysis
You can use sentiment analysis in various ways to cater to customer wants and needs:
Social media monitoring
Use a social listening tool to monitor social media and get an overall picture of your users' feelings about your brand, certain topics, and products. You can even monitor how users feel about your closest competitors. Identify urgent problems before they become PR disasters—like outrage from customers if features are deprecated, or their excitement for a new product launch or marketing campaign.
Because different audiences use different channels, conduct social media monitoring for each channel to drill down into each audience’s sentiment. For example, your audience on Instagram might include B2C customers, while your audience on LinkedIn might be mainly your staff. These audiences are vastly different and may have different sentiments about your company.
Brand monitoring
Social media isn’t the only place people talk about your company. Take into account news articles, media, blogs, online reviews, forums, and any other place where people might be talking about your brand. This helps you understand how customers, stakeholders, and the public perceive your brand and can help you identify trends, monitor competitors, and track brand reputation over time.
Start by using a sentiment analysis tool to track mentions across various channels, including brand name, product name, hashtags, and keywords related to your brand. Analyze the sentiment behind conversations and understand positive and negative feelings and opinions, including areas of improvement and potential issues. (Remember, more mentions don’t always equal more positive feelings.)
Then, benchmark sentiment performance against competitors and identify emerging threats.
Improved customer experience
Good customer service positively affects your customers and team members. The feedback can inform your approach, and the motivation and positive reinforcement from a great customer interaction can be just what a support agent needs to boost morale.
Sentiment analysis can improve the efficiency and effectiveness of support centers by analyzing the sentiment of support tickets as they come in. You can route tickets about negative sentiments to a relevant team member for more immediate, in-depth help.
Market research
Sentiment analysis can help you explore new markets, conduct competitive analyses, and identify future trends and opportunities. Some ways to incorporate sentiment analysis into market research include:
- Product feedback analysis. Understand the sentiment associated with a particular product or its features and functionalities to uncover recurring themes.
- Advertising and campaign evaluation. Use sentiment analysis to identify whether your customers love or hate your latest campaign.
- Market trend identification. Uncover consumer sentiment toward new products, innovations, and industry developments.
- Competitive analysis. Benchmark sentiment scores against your competitors, and identify strengths and weaknesses to capitalize on opportunities.
Sentiment analysis FAQ
What is the difference between sentiment analysis and semantic analysis?
Sentiment analysis is the larger practice of understanding the emotions and opinions expressed in text. Semantic analysis is the technical process of deriving meaning from bodies of text. In other words, semantic analysis is the technical practice that enables the strategic practice of sentiment analysis.
How is NLP used in sentiment analysis?
Natural language processing techniques are used in sentiment analysis algorithms to process and analyze text. NLP leverages multiple classification techniques such as:
- Stemming. Reducing a word to its root form.
- Tokenization. Breaking up a string of text into word units or tokens.
- Part-of-speech tagging. Tagging tokens with a speech category such as verb, adjective, noun, etc.
- Parsing. How words are connected and sentences are structured.
- Lexicons. A list of words and expressions.
What is the main goal of sentiment analysis?
The main goal of sentiment analysis is to determine the sentiment or feeling conveyed in text data and categorize it as positive, negative, or neutral.
Why is sentiment analysis important?
Sentiment analysis helps you gain insights into customer feedback, brand perception, or public opinion to improve on your business’s weaknesses and expand on its strengths.
