Modeling Dyadic Conversations for Personality Inference
This addresses the problem of unreliable personality inference from social media content for researchers and practitioners, though it is incremental as it builds on existing GRU methods.
The paper tackles personality inference by modeling dyadic conversations, proposing an augmented GRU model to learn Personal Conversational Embeddings, which significantly improves accuracy on a Movie Script dataset.
Nowadays, automatical personality inference is drawing extensive attention from both academia and industry. Conventional methods are mainly based on user generated contents, e.g., profiles, likes, and texts of an individual, on social media, which are actually not very reliable. In contrast, dyadic conversations between individuals can not only capture how one expresses oneself, but also reflect how one reacts to different situations. Rich contextual information in dyadic conversation can explain an individual's response during his or her conversation. In this paper, we propose a novel augmented Gated Recurrent Unit (GRU) model for learning unsupervised Personal Conversational Embeddings (PCE) based on dyadic conversations between individuals. We adjust the formulation of each layer of a conventional GRU with sequence to sequence learning and personal information of both sides of the conversation. Based on the learned PCE, we can infer the personality of each individual. We conduct experiments on the Movie Script dataset, which is collected from conversations between characters in movie scripts. We find that modeling dyadic conversations between individuals can significantly improve personality inference accuracy. Experimental results illustrate the successful performance of our proposed method.