Sentiment analysis tools help companies perceive the emotions of their customers or broader audience, by examining text data.

Some of the tools rely on tasks as basic as skimming a customer’s social media posts to differentiate positive words or statements from negative ones. Other, more sophisticated tools can mimic human intelligence and understand the nuances of emotion. At the advanced end, this technology is known as “affective computing” or “emotional artificial intelligence”—systems and devices that can recognize, interpret, process and simulate human emotions.

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During an economic recession or at times of rapidly shifting customer behaviors, sentiment analysis is a powerful way to keep a pulse on customers and even the broader population to understand changing needs and anxieties, and new moments of truth.

How companies use sentiment analysis

• Customer relationship monitoring. Companies can monitor general sentiment for products or services through indirect sources, such as social media listening and web scraping.
• Competitive positioning. Companies scan social media to assess their relative brand position and share of voice, complementing other benchmarking methods.
• Employee sentiment monitoring. Businesses use sentiment detection to identify and support underperforming teams or projects, boost morale and preempt employee attrition.
• Recommendation engine improvement. Emotional cues from customers can help improve search engine functionality and the relevance of results.
• E-learning applications. Some e-learning tools can use biometric signals to adapt to a user’s emotional state, which could enhance learning.
• Product launches or campaigns. Companies apply sentiment analysis to social media and their own communication channels to understand the effect of new product releases or campaigns, so that they can quickly adjust their approach.

Key considerations

• Use the technology as part of a broader approach. These tools should not be the only way that companies embed the voice of their customers into their operations, but rather part of a broader approach to obtaining feedback from customers and managing their experience to enable decisions and action.
• Embed quality-control measures. While most sentiment-detection algorithms use artificial intelligence and deep learning to continuously improve, they are far from perfect. Companies should design checks and balances to ensure that the resulting insights improve decision making.
• Protect privacy and data. Analyzing common data sources, such as information scraped from websites, social posts or employee emails, can raise significant privacy concerns and may even be illegal in certain contexts. Leading companies must have a robust compliance and privacy-management framework and processes in place.