  Distributed word representations have been shown to be very useful in various
natural language processing (NLP) application tasks. These word vectors learned
from huge corpora very often carry both semantic and syntactic information of
words. However, it is well known that each individual user has his own language
patterns because of different factors such as interested topics, friend groups,
social activities, wording habits, etc., which may imply some kind of
personalized semantics. With such personalized semantics, the same word may
imply slightly differently for different users. For example, the word
"Cappuccino" may imply "Leisure", "Joy", "Excellent" for a user enjoying
coffee, by only a kind of drink for someone else. Such personalized semantics
of course cannot be carried by the standard universal word vectors trained with
huge corpora produced by many people. In this paper, we propose a framework to
train different personalized word vectors for different users based on the very
successful continuous skip-gram model using the social network data posted by
many individual users. In this framework, universal background word vectors are
first learned from the background corpora, and then adapted by the personalized
corpus for each individual user to learn the personalized word vectors. We use
two application tasks to evaluate the quality of the personalized word vectors
obtained in this way, the user prediction task and the sentence completion
task. These personalized word vectors were shown to carry some personalized
semantics and offer improved performance on these two evaluation tasks.
