THEMATIC ANALYSIS OF CHATBOTS ON SOCIAL MEDIA

Year 2024, , 766 - 779, 10.10.2024

Zeynep Aytaç

https://doi.org/10.25287/ohuiibf.1419988

Abstract

Chatbots and virtual assistants that provide voice and texts have been widely used by users and customers with the latest developments in the field of artificial intelligence. In this study, it was aimed to collect tweets matching the keyword 'chatbot' and to reveal thematic distribution in the determined areas. Four important features of chatbots (chat/conversation, accessibility, integration and emotion) were taken into account. In the study, it is aimed to reveal the thematic disribution of chatbots with a total of 153093 posts via Twitter API in English using word association analysis, word frequency analysis and thematic analysis techniques. In tweets containing the keyword 'chatbot', 'customer' with a rate of 8,9% and 'google' with a rate of 7,3% are at the top of the statistically significant associated words. Some of the other associated words were 'communication', 'link', 'engineer', 'service' and 'direct message'. In the conversation domain, the most frequently occurring word among the statistically significant associated words was 'automate' with 15,3%. In the field of accessibility, 46,7% used 'general' and 32,9% used 'virtual'. In the integration domain, the words 'component use' (22,4%) and 'human' (27,3%) in the emotion domain are statistically significantly associated words. When the emerging themes and sub-themes are examined, it is revealed that not only the technical features of chatbots but also their social and emotional aspects come to the fore.

Keywords

Chatbot, Social Media, Thematic Analysis

SOHBET ROBOTLARININ SOSYAL MEDYA ÜZERİNDEN TEMATİK ANALİZİ

Abstract

Yapay zekâ alanındaki son gelişmelerle, sesli ve yazılı olarak cevap verebilme imkânı sağlayan sanal asistanlar ve sohbet robotları kullanıcılar ve müşteriler tarafından yaygın bir şekilde kullanılmaya başlanmıştır. Bu araştırmada, ‘sohbet robotu’ (chatbot) anahtar kelimesi ile eşleşen tweetler toplanarak, belirlenen alanlarda tematik dağılım ortaya konulması amaçlanmıştır. Sohbet robotlarının, dört önemli özelliği (sohbet/konuşma, erişilebilirlik, entegrasyon ve duygu) dikkate alınmıştır. Çalışmada İngilizce dilinde Twitter API ile toplamda 153093 olan gönderi üzerinden kelime ilişkilendirme analizi, kelime frekans analizi ve tematik analiz teknikleri kullanılarak tematik dağılım ortaya konulması amaçlanmıştır. ‘Sohbet Robotu’ ifadesi içeren gönderilerde istatistiksel olarak anlamlı ilişkilendirilmiş kelimeler %8,9’unda ‘müşteri’ ve %7,3’ünde ‘google’ olmuştur. Ayrıca, ‘iletişim’, ‘link’, ‘mühendis’, ‘hizmet’ ve ‘doğrudan mesaj’ kelimeleri de diğer ilişkilendirilmiş kelimelerden bazılarıdır. İstatistiksel olarak anlamlı ilişkilendirilmiş sohbet/konuşma alanında en çok yer alan kelime, % 15,3 ile ‘otomatikleştirme’ sözcüğü olmuştur. Erişilebilirlik alanında, %46,7’sinde ‘genel’, %32,9’unda ‘sanal’ ifadesi yer almaktadır. Entegrasyon alanında, ‘bileşen kullanımı’ (%22,4) ve duygu alanında ‘insan’ (%27,3) sözcükleri istatistiksel olarak ilişkilendirilmiştir. Sonuç olarak, temalar ve alt temalar dikkate alındığında, sohbet robotlarının sadece teknik özellikleri değil sosyal ve duygusal yönlerinin de öne çıktığı ortaya çıkmaktadır.

Keywords

Sohbet Robotu, Sosyal Medya, Tematik Analiz

References

• Adamopoulou, E., & Moussiades, L. (2020). Chatbots: History, technology, and applications. Machine Learning with Applications, 2, 1-18.
• Alm, A., & Nkomo, L. (2020). Chatbot experiences of informal language learners: A sentiment analysis. International Journal of Computer-Assisted Language Learning and Teaching (IJCALLT), 10(4), 51-65.
• Belfin, R., Shobana, A., Manilal, M., Mathew, A., & Babu, B. (2019). A graph based chatbot for cancer patients. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 717- 721.
• Bickmore, T. W., Mitchell, S. E., Jack, B. W., Paasche-Orlow, M. K., Pfeifer, L. M., & O’Donnell, J. (2010). Response to a relational agent by hospital patients with depressive symptoms. Interacting with computers, 22(4), 289-298.
• Brandtzaeg, P. B., & Følstad, A. (2017). Why people use chatbots. In Internet Science: 4th International Conference, INSCI 2017, Thessaloniki, Greece, November 22-24, 2017, Proceedings 4 (pp. 377-392). Springer International Publishing.
• Brandtzaeg, P., & Følstad, A. (2018). Chatbots: changing user needs and motivations. Interactions, 25(5), 38–43.
• Chin, H., Lima, G., Shin, M., Zhunis, A., Cha, C., Choi, J., & Cha, M. (2023). User-chatbot conversations during the COVID-19 pandemic: study based on topic modeling and sentiment analysis. Journal of medical Internet research, 25, e40922.
• Croes, E., & Antheunis, M. (2021). Can we be friends with Mitsuku? A longitudinal study on the process of relationship formation between humans and a social chatbot. Journal of Social and Personal Relationships, 31(1), 279–300.
• Demeure, V., Niewiadomski, R., & Pelachaud, C. (2011). How Is Believability of a Virtual Agent Related to Warmth, Competence, Personification, and Embodiment? Presence: Teleoperators & Virtual Environments, 20(5), 431–448.
• Dharwadkar, R., & Deshpande, N. (2018). A medical chatbot. International Journal of Computer Trends and Technology (IJCTT), 60(1), 41-45.
• El-Ansari, A., & Beni-Hssane, A. (2023). Sentiment analysis for personalized chatbots in e-commerce applications. Wireless Personal Communications, 129(3), 1623-1644.
• Feine, J., Morana, S., & Gnewuch, U. (2019). Measuring Service Encounter Satisfaction with Customer Service Chatbots using Sentiment Analysis. 14th International Conference on Wirtschaftsinformatik, (ss.1115- 71129). February 24-27, 2019, Siegen, Germany.
• Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016). The rise of social bots. Communications of the ACM, 59(7), 96-104.
• García-Méndez, S., De Arriba-Pérez, F., González-Castaño, F. J., Regueiro-Janeiro, J. A., & Gil-Castiñeira, F. (2021). Entertainment chatbot for the digital inclusion of elderly people without abstraction capabilities. IEEE Access, 9, 75878-75891.
• Geyser, W. (2021, 08 23). Best AI Chatbot Platforms for 2021. [Web log post]. Retrieved From: https://influencermarketinghub.com/ai-chatbot-platforms/
• Guynn, J. (2016, April 12). Zuckerberg’s facebook messenger launches ‘chat bots’ platform. USA Today. Erişim adresi: https://goo.gl/GPg3EM
• Jeong, S. S., & Seo, Y. S. (2019). Improving response capability of chatbot using twitter. Journal of Ambient Intelligence and Humanized Computing, 1-14.
• Kumar, R., Ayyasamy, R. K., Sangodiah, A., Krishnan, K., Jebna, A. K., & Theam, L. J. (2023, December). Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data. In 2023 15th International Conference on Software, Knowledge, Information Management and Applications (SKIMA) (pp. 220-225). IEEE.
• Kushwaha, A., Kumar, P., & Kar, A. (2021). What impacts customer experience for B2B enterprises on using AI- enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207-221.
• Lee, C.-W., Wang, Y.-S., Hsu, T.-Y., Chen, K.-Y., Lee, H.-Y., & Lee, L.-s. (2018). Scalable Sentiment for Sequence-to-Sequence Chatbot Response with Performance Analysis. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6164-6168.
• Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5, 1– 167.
• Lucas, G. M., Gratch, J., King, A., & Morency, L. P. (2014). It’s only a computer: Virtual humans increase willingness to disclose. Computers in Human Behavior, 37, 94-100.
• Lugano, G. (2017). Virtual Assistants and Self-Driving Cars: To what extent is Artificial Intelligence needed in Next-Generation Autonomous Vehicles? 15th International Conference on ITS Telecommunications (ITST) (s. 1-5). IEEE.
• Nath, M. P. (2018). Chat Bot -An Edge to Customer Insight. International Journal of Research and Scientific Innovation (IJRSI), 5(5), 29–32. Ouerhani, N., Maalel, A., Ghezela, H., & Chouri, S. (2020). Smart Ubiquitous Chatbot for COVID-19 Assistance with Deep learning Sentiment Analysis Model during and after quarantine. 1-9.
• Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment Classification using Machine Learning Techniques. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), (s. 79–86).
• Shah, P. (2020, June 27). Sentiment Analysis Using Textblob. [Web log post]. Retrived From: https://towardsdatascience.com/my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524
• Shim, Y., Lee, H., & Fomin, V. (2019). What benefits couldn't ‘Joyn’enjoy?: The changing role of standards in the competition in mobile instant messengers in Korea. Technological Forecasting and Social Change, 139, 125-134.
• Shum, H.-y., He , X.-d., & Li, D. (2018). From Eliza to XiaoIce: challenges and opportunities with social chatbots. Frontiers of Information Technology & Electronic Engineering, 19(1), 10–26.
• Sidaoui, K., Jaakkola, M., & Burton, J. (2020). AI feel you: customer experience assessment via chatbot interviews. Journal of Service Management, 31(4), 745-766.
• Silva-Coira, F., Cortiñas, A., & Pedreira, O. (2016). Intelligent virtual assistant for gamified environments. PACIS 2016 Proceedings, 193.
• Taecharungroj, V. (2023). “What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter. Big Data and Cognitive Computing, 7(1), 35.
• Takahashi, D. (2019, 06 26). The inspiring possibilities and sobering realities of making virtual beings. [Web log post]. Retrived From: https://venturebeat.com/2019/07/26/the-deanbeat-the-inspiring-possibilities-and- sobering-realities-of-making-virtual-beings/
• textblob.readthedocs.io. (2021). [Web log post]. Retrived From: https://textblob.readthedocs.io/en/dev/quickstart.html#sentiment-analysis
• Khosravi, M., & Azar, G. (2024). Factors influencing patient engagement in mental health chatbots: A thematic analysis of findings from a systematic review of reviews. Digital Health, 10, 20552076241247983.
• Thakur, N., & Han, C. (2018). An approach to analyze the social acceptance of virtual assistants by elderly people. Proceedings of the 8th International Conference on the Internet of Things, (s. 1-6).
• Thelwall, M. (2021). Word association thematic analysis: A social media text exploration strategy. New York, NY: Morgan & Claypool.
• Tran, A., Pallant, J., & Johnson, L. (2021). Exploring the impact of chatbots on consumer sentiment and expectations in retail. Journal of Retailing and Consumer Services, 63, 1-10.
• tweepy.org. (2021). [Web log post]. Retrived From: https://docs.tweepy.org/en/stable/api.html
• Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017). Online human-bot interactions: Detection, estimation, and characterization. Proceedings of the international AAAI conference on web and social media, 11(1), 280-289.
• Vassallo, G., Pilato, G., Augello, A., & Gaglio, S. (2010). Phase Coherence in Conceptual Spaces for Conversational Agents. Semantic Computing (s. 357–371).
• Wallace, R. S. (2009). The anatomy of ALICE (pp. 181-210). Springer Netherlands.
• Widyaningrum, P., Ruldeviyani, Y., & Dharayani, R. (2019). Sentiment Analysis to Assess the Community’s Enthusiasm Towards the Development Chatbot Using an Appraisal Theory. Procedia Computer Science, 161, 723-730.
• Zhou, A., Jia, M., & Yao, M. (2017). Business of bots: How to grow your company through conversation.Topbots Inc., NY, USA.
• Zhou, L., Gao, J., Li, D., & Shum, H.-Y. (2020). The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. Computational Linguistics, 46(1), 53-93.