@techreport{oai:ipsj.ixsq.nii.ac.jp:00217372,
 author = {Haoyue, Tan and Takefumi, Ogawa and Haoyue, Tan and Takefumi, Ogawa},
 issue = {15},
 month = {Mar},
 note = {Chatbots have become a new focus on human-computer interaction (HCI) and are already widely used in some services like booking assistants and customer services. The development of natural language processing (NLP) techniques enables chatbots to understand users' intent correctly and respond human-likely, leading to their fulfillment to more complicated tasks like interviews. This research built a sentiment-aware chatbot providing users a more engaging conversational experience while detecting users' MBTI personality type from input text. This research use language model BERT to extract features and generate sentence-level embeddings of raw text. Compared to traditional methods of training machine learning algorithms with psychological lexicons, this method significantly improved overall accuracy., Chatbots have become a new focus on human-computer interaction (HCI) and are already widely used in some services like booking assistants and customer services. The development of natural language processing (NLP) techniques enables chatbots to understand users' intent correctly and respond human-likely, leading to their fulfillment to more complicated tasks like interviews. This research built a sentiment-aware chatbot providing users a more engaging conversational experience while detecting users' MBTI personality type from input text. This research use language model BERT to extract features and generate sentence-level embeddings of raw text. Compared to traditional methods of training machine learning algorithms with psychological lexicons, this method significantly improved overall accuracy.},
 title = {Sentiment-aware Interview Chatbot Based on Deep Learning Approach for Personality Detection from Text},
 year = {2022}
}