EmotionPush: Emotion and Response Time Prediction Towards Human-Like Chatbots

Recently chatbots have been widely deployed in social network messengers. To make these chatbots more human, we present EmotionPush, the first social spoken-language private dialog dataset containing instant message logs and the unique read event logs, containing a total of 162,031 message logs and...

Full description

Saved in:
Bibliographic Details
Main Authors: Chieh-Yang Huang, Lun-Wei Ku
Format: Conference Proceeding
Language:English
Subjects:
Blogs
Data models
Facebook
Message systems
Task analysis
Time factors
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Recently chatbots have been widely deployed in social network messengers. To make these chatbots more human, we present EmotionPush, the first social spoken-language private dialog dataset containing instant message logs and the unique read event logs, containing a total of 162,031 message logs and their corresponding read logs of real private conversations on Facebook Messenger. We ensure data privacy by masking all the named entities with a code composed by their type and an unique ID. In addition, we take care of the debug need by releasing messages partially by their original words and partially in the form of word embeddings. In addition, with this dataset, we propose the emotion classification task and a novel response-time prediction task to enhance the humanity of chatbots. We establish strong baselines for these two tasks. Experiment results show that EmotionPush helps to achieve over 90% accuracy for major emotion classification and 89% accuracy for response time prediction. We expect to enable chatbots to know when to respond and what message to send to encourage user responses.
ISSN:2576-6813
DOI:10.1109/GLOCOM.2018.8647331