Biased Random Walk based Social Regularization for Word Embeddings

Nowadays, people publish a lot of natural language texts on social media. Socialized word embeddings (SWE) has been proposed to deal with two phenomena of language use: everyone has his/her own personal characteristics of language use and socially connected users are likely to use language in similar ways. We observe that the spread of language use is transitive. Namely, one user can affect his/her friends and the friends can also affect their friends. However, SWE modeled the transitivity implicitly. The social regularization in SWE only applies to one-hop neighbors and thus users outside the one-hop social circle will not be affected directly. In this work, we adopt random walk methods to generate paths on the social graph to model the transitivity explicitly. Each user on a path will be affected by his/her adjacent user(s) on the path. Moreover, according to the update mechanism of SWE, fewer friends a user has, fewer update opportunities he/she can get. Hence, we propose a biased random walk method to provide these users with more update opportunities. Experiments show that our random walk based social regularizations perform better on sentiment classification.