How emoji and word embedding helps to unveil emotional transitions during online messaging
This work addresses the problem of emotion detection in text-based communication for chatbot developers, but it is incremental as it builds on existing theories and methods.
The study tackled the challenge of interpreting emotions in online chats by modeling a customer's emotional transitions during chatbot interactions using Affect Control Theory (ACT) and extending affective dictionaries with Emoji2vec embeddings. The framework successfully identified emotional changes and customer reactions, though no concrete numbers were provided.
During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express emotional feeling is an alternative approach in these types of communication. In this project, we focus on modeling a customer's emotion in an online messaging session with a chatbot. We use Affect Control Theory (ACT) to predict emotional change during the interaction. To let the customer use emojis, we also extend the affective dictionaries used by ACT. For this purpose, we mapped Emoji2vec embedding to the affective space. Our framework can find emotional change during messaging and how a customer's reaction is changed accordingly.