Multi-modal Emotion Analysis for Chatbots
| Autor: | Jeonggeun Jin, Gijoo Yang, Dongho Kim, Hae-Jong Joo |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry media_common.quotation_subject 020206 networking & telecommunications 02 engineering and technology computer.software_genre Chatbot 030507 speech-language pathology & audiology 03 medical and health sciences Modal Recurrent neural network Feeling 0202 electrical engineering electronic engineering information engineering Artificial intelligence 0305 other medical science business computer Natural language processing media_common Spoken language |
| Zdroj: | Communications in Computer and Information Science ISBN: 9783030334949 |
| DOI: | 10.1007/978-3-030-33495-6_25 |
| Popis: | Developing chatbots that can recognize the emotions of users is a challenging problem of artificial intelligence. In order to build such a system, we need to define the emotion taxonomy to cover human-like feelings. Consequently, we need to prepare a large scale training data by using the defined emotion taxonomy. In this paper, we investigate methods of representing emotions and applying them in a deep neural network model that classifies the user’s emotion into many dimensions. We also take into account auditory signals of spoken language in addition to contextual information for classifying the emotions of users. Furthermore, we tackle the compositional negation of utterances which may cause misinterpretation of the emotion in the opposite direction. Our experiment shows that our model improves the performance of baseline models significantly. |
| Databáze: | OpenAIRE |
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