SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

TL;DR

This paper introduces SHINE, a novel embedding framework that leverages heterogeneous social network data to predict sentiment links, outperforming existing methods especially in cold start scenarios.

Contribution

The paper presents a new end-to-end embedding method for heterogeneous networks that effectively predicts sentiment links using multi-source information.

Findings

SHINE outperforms state-of-the-art baselines in link prediction.

SHINE is effective in cold start scenarios.

A new dataset with sentiment, social, and profile data was created.

Abstract

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.