Name
Abstract | ||
|---|---|---|
In the context of contact centers, customers' satisfaction after a conversation with an agent is a critical issue which has to be collected in order to detect problems and improve quality of service. Automatically predicting customer satisfaction directly from system logs, without any survey or manual annotation is a challenging task of a great interest for the field of human-human conversation understanding and for improving contact center quality of service. Unlike previous studies that have focused on questions directly related to the content of a conversation, we look at a more general opinion about a service which is called the "Net Promoter Score" (NPS) where customers are considered either as promoters, detractors or neutral. On a very large corpus of chat-conversations with customer satisfaction surveys, we explore several classification scheme in order to achieve this prediction task, only using conversation logs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8683896 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Human-Human conversation mining, Net Promoter Score, Opinion Analysis, CNN models, Attention-based RNN models | Customer satisfaction,Net Promoter,Conversation,Information retrieval,Pattern recognition,Computer science,Classification scheme,Manual annotation,Contact center,Quality of service,Artificial intelligence,Opinion analysis | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérémy Auguste | 1 | 0 | 1.35 |
| delphine charlet | 2 | 163 | 25.36 |
| Géraldine Damnati | 3 | 185 | 26.15 |
| Frederic Bechet | 4 | 0 | 1.01 |
| Benoit Favre | 5 | 133 | 8.58 |