Sentiment analysis—an audience analysis method that relies on text analysis, natural language processing (NLP), and other data mining methods—is increasingly being used to determine whether audience data is negative, positive, or neutral toward a brand. Read on to learn more about how sentiment analysis works and the benefits it can offer a company’s digital marketing team.
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What Is Sentiment Analysis?
User-generated data on the internet, particularly on social media platforms and other user forums, has grown exponentially in recent years. This huge wealth of data has created an opportunity for companies to gather and analyze user opinions about products, services, and events in an analytical strategy called sentiment analysis.
Sentiment analysis is a natural language processing technique that is used to analyze large sums of opinionated data in order to extract valuable insights. Sentiment analysis is frequently used by businesses in surveys and reviews to monitor product or service feedback, ultimately helping them better understand customer needs and drive business outcomes.
How Does Sentiment Analysis Work?
There are various sentiment analysis tools that use deep learning and artificial intelligence algorithms to analyze user input. At its most basic application, sentiment analysis is used to determine the positive-negative polarity of user input. In other words, it can be used to determine whether the user approves or disapproves of the experience, service, or product.
Sentiment analysis can be done manually, with user inputs compiled and analyzed by a human, or it can be done automatically, using algorithms that are programmed to detect whether user opinion is positive, negative, or neutral. Algorithms can also be used to automatically assign a sentiment score to each instance of user input; these instances can then be combined to develop a multi-layered sentiment analysis.
How Is Sentiment Analysis Used in Marketing?
Sentiment analysis in marketing can be used for market research, brand monitoring, social media monitoring, defining the voice of the customer (VoC), and customer service. Marketing teams can use sentiment analysis to learn how customers feel about certain topics, services, experiences, or products. They can then recalibrate their products and services based on this user feedback.
Using sentiment analysis in real-time, marketing teams can detect urgent issues before problems spiral out of control. Digital marketers can also monitor how the brand sentiment evolves over time, compare it to competition, and perform other useful analyses. It can even be used to improve customer service by analyzing customer support interactions based on user feedback.
What Are the Pros of Sentiment Analysis?
With sentiment analysis, companies can better understand and manage customer data. They are able to sort user data at scale, do real-time analysis of user input, identify key emotional triggers, increase overall customer satisfaction, and find line–of-business-specific solutions.
For a business that has live interaction with customers, such as a contact center, sentiment analysis can be used to gather real-time user opinions and quickly adapt customer service according to the mood of the customer. Businesses that are more interested in long-term brand monitoring can use this method to analyze how the brand has evolved over time and what have been the key triggers for customer satisfaction.
What Are the Cons of Sentiment Analysis?
One of the major challenges of sentiment analysis is its implementation, which becomes more complicated due to the ambiguity of language posted online. For example, social media posts often contain trending hashtags, emojis, slang, links, and other data that make it difficult to decipher user sentiment.
Another challenge of sentiment analysis is that it is mostly geared toward the English language. This is a major limitation, as many global customers use other languages or a mix of languages in their online posts and user feedback.
Sentiment analysis is a form of customer service monitoring that is growing in popularity. Learn about the Best Customer Service Software here.