Autoencoder for Semisupervised Multiple Emotion Detection of Conversation Transcripts

Textual emotion detection is a challenge in computational linguistics and affective computing as it involves the discovery of all associated emotions expressed in a given piece of text. It becomes even more difficult when applied to conversation transcripts, as there arises a need to model the spoken utterances between speakers while keeping in mind the context of the entire conversation. In this paper, we propose a semisupervised multi-label method of predicting emotions from conversation transcripts. The corpus contains conversational quotes extracted from movies. A small number of them are annotated, whereas the rest are used for unsupervised training. The word2vec word-embedding method has been used to build an emotion lexicon from the corpus and then embed the utterances into vector representations. A deep-learning auto-encoder is then used to discover the underlying structure of the unsupervised data. We fine-tune the learned model based on labeled training data and measure its performance on a test set. The experiment result suggests that the method is effective and is only slightly less effective than human annotators.