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.
If you have been following our blog, you probably have noticed that this year we have been publishing a lot about chatbots. Indeed, most of the companies in the AI sector have done the same and it is not a coincidence. As messaging apps usage grows, chatbot adoption increases. Every company wants to offer their clients more value and new channels of personalized communication.
Since 2010 automated sentiment analysis has been a source of debate around the Net with questions like: What is this methodology? How does sentiment analysis work? In what context is it useful for a business? But the truth is that what is useful for a company may not be for others. In this post we will answer some of these questions:
Searches about "sentiment analysis" are increasing, particularly related to Twitter. However not all the tools avaliable in the market can face the problem analyzing 140 characters. In this post we will show you:
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Real comments analized
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See how text is structured
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Short texts are not an issue