Annals of Computer Science and Information Systems, Volume 21

DOI: http://dx.doi.org/10.15439/2020F63

Citation: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 21, pages 193–198 (2020)

Full text

Abstract. 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.

References

1. B. Liu, “Sentiment Analysis and Opinion Mining”, 2012, pp. 11-19.
2. B. Hancock, A. Bordes, P.-E. Mazaré and J. Weston , “Learning from Dialogue after Deployment: Feed Yourself, Chatbot!,” CoRR abs/1901.05415, Madison, WI, 2019,
3. C. J. Hutto and E. Gilbert, “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text.,” 2014
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.
7. G. Salton and C. Buckley, “Term-weighting Approaches in Automatic Text Retrieval,” in Inf. Process. Manage. vol. 24 num. 5 , Tarrytown, NY, 1988, pp. 513–523.
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