Shi Hui Ng
ABSTRACT
The accessibility to affordable and yet effective mental health support is limited due to various barriers. Given the proliferation of technology, chatbots for mental health support has been widely used. Being mindful of the users’ cultural background and the ability to respond with empathy are perceived as important factors that contribute to the usability and effective communication with chatbots. Nonetheless, cultural adaptation and emotional sensitivity in mental health chatbots are not thoroughly investigated. Hence, this work aims to design and implement an emotion-aware chatbot which incorporates cultural-adaptation that could provide effective Cognitive Behavioural Therapy (CBT) interventions to Malaysian community. The emotion detection model was developed using BERT and achieved an accuracy of 0.89. For cultural adaptation, besides localised contents, Google Cloud Translation API was used as the machine translation model between Malay to English. A user study was then carried out to assess the effectiveness of emotion sensitivity and cultural adaptation in CBT-based mental health support. The ablation study shows that CBT, cultural adaptation and emotional sensitivity have positive impact on the effectiveness and usability of mental health chatbots.
- V. C. Aasha and Amal Ganesh. 2015. Rule based machine translation: English to Malayalam: A survey. In 3rd International Conference on Advanced Computing, Networking and Informatics, ICACNI 2015. Bhubaneshwar, India. https://doi.org/10.1007/978-81-322-2538-6_46Google Scholar
Cross Ref
- Manuel Benedicto. 2020. Figure Eight Labelled Textual Dataset. https://www.kaggle.com/manuelbenedicto/figure-eight-labelled-textual-datasetGoogle Scholar
- Sandra Bucci, Matthias Schwannauer, and Natalie Berry. 2019. The digital revolution and its impact on mental health care. Psychology and Psychotherapy: Theory, Research and Practice 92, 2 (March 2019), 277–297. https://doi.org/10.1111/papt.12222Google ScholarCross Ref
- Andrew C. Butler, Jason E. Chapman, Evan M. Forman, and Aaron T. Beck. 2006. The empirical status of cognitive-behavioral therapy: A review of meta-analyses. Clinical Psychology Review 26, 1 (January 2006), 17–31. https://doi.org/10.1016/j.cpr.2005.07.003Google ScholarCross Ref
- Gillian Cameron, David Cameron, Gavin Megaw, Raymond Bond, Maurice Mulvenna, Siobhan O’Neill, Cherie Armour, and Michael McTear. 2018. Best practices for designing chatbots in mental healthcare – A case study on iHelpr. In Proceedings of the 32nd International BCS Human Computer Interaction Conference. Belfast, UK. https://doi.org/10.14236/ewic/HCI2018.129Google ScholarCross Ref
- Paisarn Charoenpornsawat, Virach Sornlertlamvanich, and Thatsanee Charoenporn. 2002. Improving translation quality of rule-based machine translation. In Proceedings of the 2002 COLING workshop on Machine translation in Asia. Taipei, Taiwan. https://doi.org/10.3115/1118794.1118799Google ScholarDigital Library
- Kerstin Denecke, Sayan Vaaheesan, and Aaganya Arulnathan. 2021. A mental health chatbot for regulating emotions (SERMO) - concept and usability test. IEEE Transactions on Emerging Topics in Computing 9, 3 (2021), 1170–1182. https://doi.org/10.1109/TETC.2020.2974478Google ScholarCross Ref
- Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019. Minneapolis, Minnesota. https://doi.org/10.18653/v1/N19-1423Google ScholarCross Ref
- Ahmed Fadhil and Gianluca Schiavo. 2019. Designing for Health Chatbots. CoRR abs/1902.09022 (2019). arXiv:1902.09022http://arxiv.org/abs/1902.09022Google Scholar
- Kathleen Kara Fitzpatrick, Alison Darcy, and Molly Vierhile. 2017. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A randomized controlled trial. JMIR Ment Health 2017 4, 2 (2017), e19. https://doi.org/10.2196/mental.7785Google ScholarCross Ref
- Derek Griner and Timothy B. Smith. 2006. Culturally adapted mental health intervention: A meta-analytic review. special issue: Culture, race, and ethnicity in psychotherapy. Psychotherapy 43, 4 (December 2006), 531–548. https://doi.org/10.1037/0033-3204.43.4.531Google ScholarCross Ref
- Becky Inkster, Shubhankar Sarda, and Vinod Subramanian. 2018. An empathy-driven, conversational artificial intelligence agent (wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth 6, 11 (2018), e12106. https://doi.org/10.2196/12106Google ScholarCross Ref
- Rafiya Jan and Afaq Alam Khan. 2018. Emotion Mining Using Semantic Similarity. International Journal of Synthetic Emotions (IJSE) 9, 2 (July 2018), 1–22. https://doi.org/10.4018/IJSE.2018070101Google ScholarDigital Library
- Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M. Rush. 2017. OpenNMT: Open-source toolkit for neural machine translation. In 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017. Vancouver, Canada. https://doi.org/10.18653/v1/P17-4012Google ScholarCross Ref
- Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. In Proceedings of The 8th International Joint Conference on Natural Language Processing, IJCNLP 2017. Taipei, Taiwan.Google Scholar
- Yu-Zane Low, Lay-Ki Soon, and Shageenderan Sapai. 2020. A Neural Machine Translation Approach for Translating Malay Parliament Hansard to English Text. In 2020 International Conference on Asian Language Processing, IALP 2020. Kuala Lumpur, Malaysia. https://doi.org/10.1109/IALP51396.2020.9310470Google ScholarCross Ref
- Kien Hoa Ly, Ann-Marie Ly, and Gerhard Andersson. 2017. A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet Interventions 10 (December 2017), 39–46. https://doi.org/10.1016/j.invent.2017.10.002Google ScholarCross Ref
- Saif M. Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018. SemEval-2018 Task 1: Affect in Tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation(SemEval-2018). Louisiana, USA. https://doi.org/10.18653/v1/s18-1001Google ScholarCross Ref
- Jooyoung Oh, Sooah Jang, Hyunji Kim, and Jae-Jin Kim. 2020. Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder. International Journal of Medical Informatics 140 (August 2020). https://doi.org/10.1016/j.ijmedinf.2020.104171Google ScholarCross Ref
- Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, Gautam Naik, Erik Cambria, and Rada Milhalcea. 2019. MELD: A multimodal multi-party dataset for emotion recognition in conversations. In 57th Annual Meeting of the Association for Computational Linguistics, ACL 2019. Florence, Italy.Google ScholarCross Ref
- Praveen. 2020. Emotions dataset for NLP. https://www.kaggle.com/praveengovi/emotions-dataset-for-nlpGoogle Scholar
- Sandeep Saini and Vineet Sahula. 2015. A survey of machine translation techniques and systems for Indian languages. In 2015 IEEE International Conference on Computational Intelligence and Communication Technology, CICT 2015. Ghaziabad, India. https://doi.org/10.1109/CICT.2015.123Google ScholarCross Ref
- Aditya Nrusimha Vaidyam, Hannah Wisniewski, John David Halamka, Matcheri S. Kashavan, and John Blake Torous. 2019. Chatbots and conversational agents in mental health: A review of the psychiatric landscape. Canadian Journal of Psychiatry 64, 7 (March 2019), 456–464. https://doi.org/10.1177/0706743719828977Google ScholarCross Ref
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In 31st Annual Conference on Neural Information Processing Systems, NIPS 2017. Long Beach, USA. https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdfGoogle Scholar
- Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016. Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. CoRR abs/1609.08144 (2016). arXiv:1609.08144http://arxiv.org/abs/1609.08144Google Scholar
- Ashima Yadav and Dinesh Kumar Vishwakarma. 2020. Sentiment analysis using deep learning architectures: a review. Artificial Intelligence Review 53 (2020), 4335–4385. https://doi.org/10.1007/s10462-019-09794-5Google ScholarDigital Library
- Ali Yadollahi, Ameneh Gholipour Shahraki, and Osmar R. Zaiane. 2017. Current state of text sentiment analysis from opinion to emotion mining. Comput. Surveys 50, 2 (June 2017), a25. https://doi.org/10.1145/3057270Google ScholarDigital Library
- Jianzhong Zheng, Xiaoliang Chen, Yajun Du, Xianyong Li, and Jiabo Zhang. 2019. Short text sentiment analysis of micro-blog based on BERT. In 13th International Conference on Multimedia and Ubiquitous Engineering, MUE 2019 and 14th International Conference on Future Information Technology, Future Tech 2019. Xian, China. https://doi.org/10.1007/978-981-32-9244-4_56Google ScholarCross Ref
Index Terms
- Emotion-Aware Chatbot with Cultural Adaptation for Mitigating Work-Related Stress
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