Automated NER, Sentiment Analysis and Toxic Comment Classification for a Goal-Oriented Chatbot

This paper focuses on improving the conversational ability of a robo receptionist. In particular, we seek to improve the ability to retrieve information specific to an organization through the design of a named-entity-recognition module. We accentuate the chatbot's sensitivity to a user's comment and the tone of a conversation through designing a fine-grained sentiment analysis module. And, finally, we have ensured the output of the self-learning chatbot is positive and pleasant through a toxic-comment classifier that improves upon a dictionary-based profanity detection module. Improving the core components of the chatbot, viz., the named entity recognition, sentiment analysis and toxic comment classification modules, reflect as an improvement in the performance of the chatbot. The performance of these modules in comparison with predecessor approaches and the code to reproduce the results have also been included to facilitate further improvements in these directions.