Natural language processing (NLP) is a field of computer science that deals with the analysis and interpretation of natural language. It can be used to understand and interact with people, including in chatbots, customer service, and digital assistants.
One popular application of natural language processing api is conversational analytics, which uses NLP to analyze interactions between humans and machines. This gives companies insight into how people are using their products and services, and helps them make changes that improve user experience.
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Some of the most common tools used for conversational analytics include natural language processing engines (NLEs), text analytics, machine learning, and big data. Each has its own strengths and weaknesses, so it’s important to choose the right one for the task at hand.
There are many different NLP solutions available on the market, but the best ones are usually those that have been specifically designed for conversational analysis. Some of the more well-known conversation analytics software include IBM Watson Conversation Service, Microsoft Cognitive Services Bot Framework, PureLang AI, Apple’s CoreML Speech Recognition Library, Google’s Dialogflow Natural Language Processor, Amazon’s Rek cognition, and Nuance’s Dragon software.
In general, you can choose any tool that fits your needs, but for conversational analysis, you should focus on NLP tools that have been specifically designed for the task. This means that they will be better suited to help you understand what people are saying in conversations, especially when it comes to sentiment analysis.
