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.

While a picture may be worth a thousand words – a thousand words may be worth thousands of dollars. Never thought how valuable all your company unstructured data would be? This heterogeneous knowledge can turn out to be quite useful for companies, however, there is still much to be learned. 

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?

Customers are using channels such as Facebook Messenger as the place where to complain when their online order is delivered late. Therefore, companies have started to trust in chatbots for handling these issues. Can you see the potential of applying sentiment analysis to chatbot conversations?

Your hotel deals with thousands of reviews from TripAdvisor, Yelp and your own satisfaction surveys. Nevertheless, nobody can process all this information and get accurate insights of what is going on. That's why, companies start relying on machines to do this task automatically by using sentiment analysis tools.