Prominent User Segments in Online Consumer Recommendation Communities: Capturing Behavioral and Linguistic Qualities with User Comment Embeddings

Online conversation communities have become an influential source of consumer recommendations in recent years. We propose a set of meaningful user segments which emerge from user embedding representations, based exclusively on comments’ text input. Data were collected from three popular recommendation communities in Reddit, covering the domains of book and movie suggestions. We utilized two neural language model methods to produce user embeddings, namely Doc2Vec and Sentence-BERT. Embedding interpretation issues were addressed by examining latent factors’ associations with behavioral, sentiment, and linguistic variables, acquired using the VADER, LIWC, and LFTK libraries in Python. User clusters were identified, having different levels of engagement and linguistic characteristics. The latent features of both approaches were strongly correlated with several user behavioral and linguistic indicators. Both approaches managed to capture significant variability in writing styles and quality, such as length, readability, use of function words, and complexity. However, the Doc2Vec features better described users by varying level of contribution, while S-BERT-based features were more closely adapted to users’ varying emotional engagement. Prominent segments revealed prolific users with formal, intuitive, emotionally distant, and highly analytical styles, as well as users who were less elaborate, less consistent, but more emotionally connected. The observed patterns were largely similar across communities.