SNS Retrieval Based on User Profile Estimation Using Transfer Learning from Web Search
by Daisuke Kataoka, Keishi Tajima
Abstract
In this paper, we propose a method of retrieving posts on social
networking services (SNSs) by specifying a pair of queries: a topic
query and an entity query. A topic query specifies the topic of the
posts to retrieve (e.g., “iPhone”) and an entity query specifies the
type of users who posted them (e.g., “students”). In the existing
search systems for SNS posts, we can specify topics of posts by
keywords, but we cannot specify types of users. Even if we include
keywords specifying types of users in a query, such keywords are not
usually included in tweets or user profile data. In our method, we
estimate types of users by learning vocabulary whose appearance is
correlated with specific types of users. We learn it from the
datasets obtained through Web search. We retrieve Web documents
through the search with a keyword specifying the type of users (e.g.,
“student”), and we also retrieve Web documents by using a keyword
specifying its opposite (e.g., “adult”). We regard the documents
retrieved by these queries as positive and negative examples of
documents describing the target type, and we train a model for
recognizing users of the given type. We recognize users of the target
type by inputting their posts and their profile data into the model.
We use Web documents instead of SNS posts for training the model
because the Web has more documents describing types of people.