A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot
This addresses poor service management in organizations by enhancing chatbot-human interaction, though it appears incremental as it combines existing NLP tasks.
The paper tackled the problem of chatbots failing to understand personalized user inputs by incorporating sentiment analysis, emotion detection, intent classification, and named-entity recognition using deep learning, resulting in improved understanding and intelligence in chatbots.
In recent times, a large number of people have been involved in establishing their own businesses. Unlike humans, chatbots can serve multiple customers at a time, are available 24/7 and reply in less than a fraction of a second. Though chatbots perform well in task-oriented activities, in most cases they fail to understand personalized opinions, statements or even queries which later impact the organization for poor service management. Lack of understanding capabilities in bots disinterest humans to continue conversations with them. Usually, chatbots give absurd responses when they are unable to interpret a user's text accurately. Extracting the client reviews from conversations by using chatbots, organizations can reduce the major gap of understanding between the users and the chatbot and improve their quality of products and services.Thus, in our research we incorporated all the key elements that are necessary for a chatbot to analyse and understand an input text precisely and accurately. We performed sentiment analysis, emotion detection, intent classification and named-entity recognition using deep learning to develop chatbots with humanistic understanding and intelligence. The efficiency of our approach can be demonstrated accordingly by the detailed analysis.