Sentiment Analysis is a procedure used to determine if a chunk of text is positive, negative or neutral. In text analytics, natural language processing (NLP) and machine learning (ML) techniques are combined to assign sentiment scores to the topics, categories or entities within a phrase.
A successful virtual agent must be able to hold a natural conversation with a human. Processing natural language is, however, not a piece of cake for Artificial Intelligence. The way we speak is not regular and robotic, but emotional and full of context. NLU becomes, therefore, essential for customer support bots to get what clients need.
In an era of globalization, being multilingual is essential for business. Either for your e-commerce bot to understand international customers, or for your feedback management platform to get insights from every user, English is not enough.
While AI is one of the most important trends nowadays, there are still challenges to overcome. Apart from common technical issues such as a lack of quality data, there is much beyond its abilities for an AI to effectively understand and react when it comes to human-machine interaction.
Users speak the way they choose, not as you wish. Developers are building tens of bots and almost all of them bump into the same problem: they are not properly working. Most of the bots don’t understand users and the problem lies in their training. That’s the simple truth every bot developer must face sooner or later. The question is: are they facing it the right way?