What if you could tap into how your market feels about your product, in real-time, from the moment of launch? Think about the insights you could glean from this knowledge—and the potential impact it could have on your product’s success, your brand reputation, and your company’s overall growth.
It sounds almost magical. But in today’s world of AI and machine learning, this magic has become a reality, placing the power to garner your audience’s perceptions and attitudes right at your fingertips. And it comes in the form of a natural language processing (NLP) technique called sentiment analysis.
As we’ve previously explored, NLP techniques provide the machine learning power marketers need to extract the riches from their data. And, as you’ll see below, there are good reasons why sentiment analysis has become one of the most popular techniques available to marketers today.
What is sentiment analysis?
Sentiment analysis, also known as opinion mining, takes your unstructured data—social media posts and conversations, customer feedback surveys, online reviews, etc.—and analyzes it to determine your customers’ attitudes and feelings toward your products, services, or brand. At its most basic, sentiment analysis determines whether your customers’ sentiment as expressed in the data is positive, negative, or neutral.
Depending on the complexity of the system, sentiment analysis models will employ one of the following algorithms:
- Manual, or rules-based. This type of approach uses a manually defined set of rules—for example, word lists or lexicons—to determine the sentiment within data.
- Automatic, or machine-learning-based. With this type of algorithm, the model uses machine-learning algorithms to automatically extract sentiment data from text.
- Hybrid, or combined. A hybrid algorithm combines manual and automatic approaches to obtain the best elements of both.
Common types of sentiment analysis
Access to how your audience generally thinks and feels about your brand is already a game changer, but different types of sentiment analysis can extract customer sentiment on a more granular level, to drive even more powerful insights.
- Graded. With graded or fine-grained sentiment analysis, you can refine your understanding of the polarity of your audience’s opinions. Review rating systems—like the ones found on many e-commerce sites—are one use case where graded sentiment analysis can be employed to, for example, differentiate between five-star reviews (reflecting a very positive experience) and one-star, or very negative, reviews in the review section of your site.
- Emotion detection. Emotion detection sentiment analysis goes beyond general perceptions to distill your audience’s emotions from the data (for example, are they happy, sad, angry, or frustrated?). These insights can impact everything from customer support to product improvement.
- Intent analysis. As its name implies, intent analysis takes sentiment analysis a step further by identifying words signaling customer intent within the data you’re working with: Are your customers there to purchase, or are they just browsing or researching? With this type of insight in hand, you can match your messaging to customers’ specific intents.
- Aspect-based. Aspect-based sentiment analysis ascertains the specific features or elements being discussed, as well as the sentiment pertaining to these features or elements. For example, reviews of a vehicle might be overall positive, but aspect-based analysis might show a negative perception of the vehicle’s cup holders, disclosing a flaw in product design that can be addressed in the next model.
More than just sentiment: other benefits of sentiment analysis
In addition to its ability to extract insights about audience opinions, sentiment analysis offers marketers a number of other benefits, including:
- Scalability. With sentiment analysis, you can easily analyze the mountains of first-party data you’ve accumulated or have access to—something that’s cost prohibitive to do manually.
- Customer-centric priorities. Sentiment analysis lets you prioritize your customers by keeping their perceptions and opinions at the forefront of everything you do.
- Real-time response. Whether you’re focusing on brand monitoring or customer service, sentiment analysis enables you to quickly jump into crisis management mode or take the actions necessary for customer retention.
Dealing with the quirks of human language
On the flip side, sentiment analysis also comes up against the challenges that result from the many peculiarities and ambiguities of human language—things that don’t exist within the binary environment of machine language. These challenges include:
Sarcasm. Sarcasm is the use of irony, and according to Merriam-Webster, irony is “the use of words to express something other than and especially the opposite of the literal meaning.” It’s easy to see how this can muddle the machine learning waters.
Example: When a customer says, “I really love having to wrestle with my car’s cup holder to regain control over my thermal coffee mug”, the word love, read literally, could place this sentiment squarely—and inaccurately—in the “very positive” category.
Subjectivity. Sentiment analysis is based on accurately identifying the tone of text, but it can be difficult to differentiate between a subjective statement and an objective one—and this differentiation is crucial for determining tone.
Example: The sentence “The views are spectacular, but the space is just too small to accommodate all my wedding guests” contains both objective and subjective elements, but despite the positive subjective opinion, the overall tone is negative.
Idioms. Human language has its fill of idioms (see what we did there?), and idioms work because they’re not meant to be taken literally. Machine algorithms, on the other hand, usually come at things from a very literal perspective, which can lead to inaccurate interpretations of sentiment.
Example: In the statement, “This scarf is gorgeous, but it’s going to break the bank,” the sentiment analysis model might ignore the negative opinion expressed by “it’s going to break the bank,” if it evaluates the phrase in its literal sense, rather than understanding the impact the idiom has on overall sentiment.
Context. We humans operate quite comfortably with context, since we’re used to having to rely on it to interpret language on a daily basis. But machine language doesn’t work well with context, and when you throw in a dash of word ambiguity, the challenge becomes clear.
Example: The correct interpretation of context is required to assign an accurate sentiment to the statement, “I can’t recommend this microwave oven more highly.”
Fortunately, these human language issues aren’t insurmountable, and the solution typically lies in training your sentiment analysis model to address the specific linguistic challenges it faces.
Popular sentiment analysis use cases
Sentiment analysis can be employed in any use case that could benefit from insights into your audience’s perceptions or attitudes. Which ultimately means you can use sentiment analysis for a host of use cases, including:
Social media monitoring. In today’s social media-influenced world, the adage that “any publicity is good publicity” no longer holds true. A tweet or TikTok video about your brand might go viral, but viral doesn’t always mean good PR. Sentiment analysis lets you determine the subjective sentiment behind social media chatter about your brand. This enables you to gauge public perception of your brand, respond quickly to negative comments, and avoid potential PR disasters.
Competitive research. You don’t have to engage in corporate espionage to find out what your target market thinks of your competition. With sentiment analysis, you can make use of other data sources, such as high-quality data from select data providers and publicly available data, to see which of your competitors your audience is responding favorably to, and why.
Enhanced customer service. Customer support systems handle a voluminous amount of information on a daily basis. Sentiment analysis can help you streamline your support team’s workflows by prioritizing emails, messages, and tickets that are in need of urgent attention.
Marketing messages. Insights about customers’ intent enable the personalization of marketing messages to more precisely target where they’re at within your sales funnel. With sentiment analysis, you can craft messages to meet customers’ needs, from discount codes for those near the bottom of the funnel to educational resources for those who are still researching.
Conclusion
NLP techniques place the power of AI and machine learning squarely within marketers’ hands. Gone are the cost-prohibitive days of manually processing the data you’ve collected. And when you supplement this data with other rich data sources, such as the data solutions offered by ShareThis, you can use sentiment analysis to garnish insights over an even wider lens. With its ability to extract key sentiment information from mounds of data, it’s easy to see why sentiment analysis is one of the most widely used NLP techniques.
