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Sentimental Analysis for AIML-Based E-Health Conversational Agents

Citation

Ireland, D and Hassanzadeh, H and Tran, SN, Sentimental Analysis for AIML-Based E-Health Conversational Agents, Neural Information Processing: ICONIP 2018. Lecture Notes in Computer Science, volume 11302, 13-16 December 2018, Siem Reap, Cambodia, pp. 41-51. ISBN 978-3-030-04178-6 (2018) [Refereed Conference Paper]

Abstract

Conversational agents or chat-bots are emerging in various applications including finance, education and e-health. Recent research has highlighted the importance of the consistency between the response of the chat-bot and the sentiment of the input utterance. This is quite challenging as detecting the sentiment of an utterance often depends on the context and timing of the conversation. Moreover, whereas humans have complex repair strategies, encoding these for human-computer interaction is problematic. This paper presents five sentiment prediction models for conversational agents that are trained on a large corpus of smartphone application reviews and their sentiment ranks obtained from the Google playstore. These models are tested on collected, real-life conversations between a human and a machine. It is found that positive utterances are classified with a high accuracy but classifying negative utterances is still challenging.

Item Details

Item Type:Refereed Conference Paper
Keywords:sentiment analysis, E-health chatbots, conversation agent
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Intelligent robotics
Objective Division:Health
Objective Group:Provision of health and support services
Objective Field:Mental health services
UTAS Author:Tran, SN (Dr Son Tran)
ID Code:139908
Year Published:2018
Deposited By:Information and Communication Technology
Deposited On:2020-07-14
Last Modified:2020-07-14
Downloads:0

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