Sentiment Analysis Tools enable organizations to analyze all forms of text data to determine the overall sentiment, opinion, or emotional tone expressed by the users in their messages. These tools use technologies such as machine learning, natural language processing (NLP), text analysis, and biometrics for analyzing and breaking a large amount of text data into small chunks to identify the underlying sentiment of a message. The underline sentiment can be positive, negative, or neutral, which is calculated by assigning the sentiment score based on a pre-determined scale to each chunk. These tools also provide multilingual support to enable sentiment analysis across different languages. These tools are commonly used in marketing, customer support, e-commerce, and finance.
Gartner defines a voice of the customer (VoC) platform as one that integrates feedback collection, analysis and action into a single interconnected platform that helps understand and improve the customer experience. Sources of feedback extend beyond direct surveying to include other, more indirect and inferred sources. VoC platforms enable leaders responsible for functions such as customer service, marketing, or sales to better manage the customer experience (CX) through a deep understanding of customer needs, motivations, goals and behaviors. The resulting insights trigger recommendations and actions across the enterprise.