SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction (1712.00732v1)

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.

Insights on "SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction"

The paper "SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction" addresses the challenge of predicting sentiment links in social networks, a task pivotal for applications such as personalized advertising and public opinion analysis. Traditional sentiment analysis often relies solely on textual data, which fails to capture the comprehensive sentiment landscape across users who may not explicitly express their opinions. This paper introduces a method for uncovering latent sentiment links through the synthesis of heterogeneous information networks, leveraging both explicit sentiment and implicit social and profile data.

Dataset Construction

Due to the scarcity of explicit sentiment annotations in prevalent social networks, the authors devised a dataset derived from Weibo, a major social platform in China. This dataset integrates sentiment relations, social connections, and user profiles, drawing on entity-level sentiment extraction techniques. The dataset enables a more holistic view of the networks by considering celebrity-focused sentiment within tweets and supplementing with users' social and personal profile data.

The SHINE Framework

The authors propose the SHINE (Signed Heterogeneous Information Network Embedding) framework as a novel mechanism for sentiment link prediction. SHINE is designed to deliver user embeddings derived from multiple, heterogeneous network sources through a combination of deep autoencoders. These autoencoders are tailored to extract features from sentiment, social, and profile networks individually, preserving network structure while projecting into a low-dimensional space. Unlike previous models limited to homogeneous or unsigned networks, SHINE can accommodate the signed and multifaceted nature of real-world networks observed in the sentiment domain.

Experimental Results

The efficacy of SHINE is demonstrated on two real-world datasets. The experimental results underscore SHINE's superiority in link prediction and node recommendation tasks when benchmarked against state-of-the-art models. Notably, SHINE achieved accuracy improvements ranging from 8.8% to 16.8% over baseline methods. Moreover, in node recommendation, SHINE demonstrated its capability of harnessing side information, facilitating robust performance even in cold-start scenarios where new users have scant interactions within the network.

Implications and Future Directions

The implications of SHINE extend to both practical and theoretical domains. Practically, SHINE could significantly enhance prediction models used in social media analytics, enabling a deeper understanding of user interactions and sentiment dynamics. Such insights could translate into improved recommender systems or more nuanced sentiment analyses for market research and influence prediction. Theoretically, SHINE bridges a critical gap in current network embedding methodologies by addressing the complexities of real-world, signed, heterogeneous data structures.

Future research can explore several potential pathways to extend the SHINE framework. For instance, integrating more complex profile data or incorporating time-based sentiment dynamics could further enrich the model's predictive capabilities. Additionally, adaption of SHINE to other domains, such as recommendations with implicit feedback or multi-layered social networks, could offer valuable insights beyond the current scope.

In conclusion, this paper contributes a significant advancement in the field of network embedding and sentiment analysis by offering a robust framework tailored to the intricate realities of heterogeneous information networks. SHINE serves not only as a practical tool but also pushes the boundaries of how sentiment can be understood within complex social ecosystems.