Sentiment analysis, in its simplest terms, is a technique for measuring how customers respond to a product, service or brand. Leveraging natural language processing and text analysis, sentiment analysis is employed in several ways today, most commonly to assess customer comments and feedback. For example, several tools exist that analyze comments made via customer feedback channels or social media to assess whether the response is positive, negative and neutral. From there, organizations can use the insights gained to drive changes in products, customer service, Web sites, customer communications, marketing campaigns, etc.
Proactively using Sentiment Analysis to Steer the Conversation
Everyone in customer servicing and marketing understands that using just the right tone is key to communicating with customers effectively. Sentiment analysis has been referred to as an “emotional spell-check” that can uncover hidden emotions and attitudes in a message. Using these technologies to analyze any kind of content can help ensure that communications convey the desired feeling.
Progressive organizations, particularly in financial services, are leveraging sentiment analysis to assess the subtleties in their marketing and servicing communications to customers. When applied across all channels of outbound communications, it not only helps teams craft messages that elicit the desired emotional response from their audience, but it is also a powerful way in which brands can drive greater consistency across the customer experience.
In short, using sentiment analysis is a key way in which organizations can both learn what to say to customers and, more importantly, what not to say to keep customers receptive.
How does it work?
Sentiment analysis applications classify text-based information as positive, negative, or neutral. In the simplest of examples, the words “growth”, “successful”, and “increase” can result in a message being classified as positive, while “risk” and “fall” will be classified as negative. Based on this analysis, a sentiment engine can gain an understanding of the general sentiment of the message, but the right classification isn’t always so straightforward.
Today’s Content Intelligence technologies can take the understanding of sentiment to a deeper level that isn’t simply based on finding words that would be considered positive or negative. These solutions leverage artificial intelligence (AI) to understand the meaning and intent behind the message to provide more accurate answers and useful guidance to a writer. This can also extend to the ability to detect very subtle nuances such as sarcasm. Let’s look at some simple examples to see how this works.