A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot

TL;DR

This paper presents a deep learning-based approach to enhance chatbots with human-like understanding by integrating sentiment analysis, emotion detection, intent classification, and named-entity recognition, aiming to improve user interaction quality.

Contribution

The study introduces a comprehensive deep learning framework that combines multiple NLP tasks to enable chatbots to understand user inputs more accurately and humanistically.

Findings

Improved accuracy in sentiment and emotion detection.

Enhanced intent classification performance.

Better extraction of named entities from user input.

Abstract

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.