Classifying Emotions in Customer Support Dialogues in Social Media
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
EmoTwiCS: A Corpus for Modelling Emotion Trajectories in Dutch Customer Service Dialogues on Twitter
Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of these online platforms, the automatic detection of emotions forms an…
An Emotional Journey: Detecting Emotion Trajectories in Dutch Customer Service Dialogues
The ability to track fine-grained emotions in customer service dialogues has many real-world applications, but has not been studied extensively. This paper measures the potential of prediction models on that task, based …
SocialNLP EmotionX 2019 Challenge Overview: Predicting Emotions in Spoken Dialogues and Chats
We present an overview of the EmotionX 2019 Challenge, held at the 7th International Workshop on Natural Language Processing for Social Media (SocialNLP), in conjunction with IJCAI 2019. The challenge entailed predicting…
KODIS: A Multicultural Dispute Resolution Dialogue Corpus
We present KODIS, a dyadic dispute resolution corpus containing thousands of dialogues from over 75 countries. Motivated by a theoretical model of culture and conflict, participants engage in a typical customer service d…
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies…
Emotion ClassificationGeneral ClassificationSentence ClassificationSentence Embedding