Sentiment analysis, also known as opinion mining, is the practice of assessing customer input to determine attitudes, emotions and opinions about brands, product lines, marketing campaigns, and more. A powerful sentiment analysis model relies on natural language processing (NLP), machine learning, and computational linguistics to data mine sources like social media comments, blogs, and product reviews for relevant input. This data is typically scored as positive, neutral, or negative sentiment and made available through reporting tools.
Sentiment analysis algorithms allow brands to stay on top of consumer opinion and intervene where possible. For example, by monitoring reviews of their businesses and products, brands can take actions such as addressing negative sentiment online, reaching out directly to dissatisfied customers, and making product improvements. Monitoring reviews with positive sentiment has become increasingly important as more consumers, especially younger ones, rely on them for purchase decisions. Staying abreast of what customers are saying online is a complex task that's made simpler by aspect-based sentiment analysis tools.
In addition to monitoring a brand's online reputation, sentiment analysis tools have powerful applications in contact centers. They can be used to assess the nature of customer comments in phone calls, text messages, emails, and chat sessions. These contacts can then be routed based on customer sentiment to, for example, agents who are good at soothing angry customers. Additionally, sentiment scores can be calculated down to the agent level, which can help identify both best practices and coaching opportunities. When paired with other customer experience measures, such as Net Promoter Scores, sentiment analysis models help provide a more holistic view of customer experience.
