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For example, users of today won’t want to forego a series of questions when interacting with a chatbot - the bot of the future will already have the user’s browser history, social media information, past purchases, etc.Users tend to go to chatbots for simple questions, product recommendations, or to quickly purchase or book something.If a user can easily click one or two buttons and reorder something, they will prefer not to speak to a chatbot or virtual assistant.Alternatively, if a consumer used a bot to book flights, the bot would most likely ask the user too many questions and would defeat the purpose of the bot.Statista reports that the size of chatbot market is was 113 million USD in 2015 and projected to be 994.5 million USD in 2024 Mc Kinsey reports that in 2015 30% of all customer care interactions took place through chat, social media, and email; this is projected to rise to 48% in 2020.While chatbot use and investment are booming, they have a long way to go.While advanced natural language processing is important, most people do not go on the internet to simply chat - they want the bot to effectively execute whatever task it is asked to do.Sentiment analysis includes parsing out specific target phrases and the sentiment of the entire text.
In addition, bots should maintain the context and its state with all parameters during a single session in order for the user to get the result he/she is looking for.
These bots were put forth in Amazon’s Mechanical Turk, or an online crowdsourcing marketplace that enables individuals and businesses to bargain.
Most users believed that they were interacting with a human and not a bot.
With natural language understanding, developers can analyze semantic features of text input such as categories, concepts, emotion, entities, keywords, metadata, relations, semantic roles, and sentiment.
Web developer kits like IBM Watson’s chatbot kit allows for easy use of NLU.