Measuring the Polarity of Conversations between Chatbots and Humans: A Use Case in the Banking Sector

• Autorzy: Le Noé-Bienvenu Guillaume , Nouvel Damien , Mostefa Djamel

Identyfikatory

DOI

10.15439/2020F63

• Konferencja: Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

This paper describes a study on opinion analysis applied to both human to chatbot conversations, but also to human to human conversations using data coming from the banking sector. A polarity classifier SVM model applied to conversations provides insights and visualisations of the satisfaction of users at a given time and its evolution. We conducted a study on the evolution of the opinion on the conversations started with the chatbot and then transferred to a human agent. This work illustrates how opinion analysis techniques can be applied to improve the user experience of the customers but also detect topics that generate frustrations with a chatbot or with human experts.

Słowa kluczowe

EN

banking; data visualisation; natural language interfaces; support vector machine; text analysis

PL

bankowość; wizualizacja danych; interfejs w języku naturalnym; analiza tekstu; maszyny wektorów wspierających

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2020
• Tom: Vol. 21
• Strony: 193--198
• Opis fizyczny: Bibliogr. 14 poz., tab., wz., il.

Twórcy

autor

Le Noé-Bienvenu Guillaume

• OrangeBank, 67 Rue Robespierre, 93100 Montreuil, France

autor

Nouvel Damien

• Inalco ERTIM, 2 Rue de Lille, 75007 Paris, France

autor

Mostefa Djamel

• OrangeBank, 67 Rue Robespierre, 93100 Montreuil, France

Bibliografia

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• 4. E. Andrea and S. Fabrizio, “SENTIWORDNET: A publicly available lexical resource for opinion mining,” in Proceedings of the 5th Conference on Language Resources and Evaluation (LREC), 2006
• 5. L. Joseph, E. Morin and S. Peña Saldarriaga, “CANÉPHORE : un corpus français pour la fouille d’opinion ciblée,” in Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles, Caen, France, 2015, pp. 418–424.
• 6. L. Zhang and S. Ferrari, “Intensité et polarité : un modèle opératoire articulant plusieurs travaux linguistiques,” in Langue française, (num 184), 2014, pp. 35–54.
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• 8. J. Thorsten, “Text Categorization with Support Vector Machines: Learning with Many Relevant Features,” 1998
• 9. B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs Up: Sentiment Classification Using Machine Learning Techniques,” in Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing vol. 10, Stroudsburg, PA, 2002, pp. 79–86
• 10. J. Lilleberg, Y. Zhu, and Y. Zhang, “Support vector machines and Word2vec for text classification with semantic features,” in IEEE, 2015/07, pp. 136-140
• 11. Y. Kim, “Convolutional Neural Networks for Sentence Classification,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP, Doha, Qatar, 2014, pp. 1746–1751
• 12. T. Hamon, A. Fraisse, P. Paroubek, P. Zweigenbaum and C. Grouin, “Analyse des émotions, sentiments et opinions exprimés dans les tweets: présentation et résultats de l’édition 2015 du défi fouille de texte (DEFT),” in Actes de la 22e conférence sur le Traitement Automatique des Langues Naturelles (TALN 2015), 2015, pp. A20.
• 13. L. Buitinck, et al., “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Workshop: Languages for Data Mining and Machine Learning, Madison, WI, 2013, pp. 108–122.
• 14. S. Bird, E. Klein and E. Loper, “Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit,” 2009

Uwagi

1. Track 1: Artificial Intelligence

2. Technical Session: 5th International Workshop on Language Technologies and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).