Exploring and Inferring User-User Pseudo-Friendship for Sentiment Analysis with Heterogeneous Networks [chapter]

Hongbo Deng, Jiawei Han, Hao Li, Heng Ji, Hongning Wang, Yue Lu
2013 Proceedings of the 2013 SIAM International Conference on Data Mining  
With the development of social media and social networks, user-generated content, like forums, blogs and comments, are not only getting richer, but also ubiquitously interconnected with many other objects and entities, forming a heterogeneous information network between them. Sentiment analysis on such kinds of data can no longer ignore the information network, since it carries a lot of rich and valuable information, explicitly or implicitly, where some of them can be observed while others are
more » ... ot. In this paper, we propose a novel information network-based framework which can infer hidden similarity and dissimilarity between users by exploring similar and opposite opinions, so as to improve postlevel and user-level sentiment classification in the same time. More specifically, we develop a new meta path-based measure for inferring pseudo-friendship as well as dissimilarity between users, and propose a semi-supervised refining model by encoding similarity and dissimilarity from both user-level and post-level relations. We extensively evaluate the proposed approach and compare with several state-of-the-art techniques on two real-world forum datasets. Experimental results show that our proposed model with 10.5% labeled samples can achieve better performance than a traditional supervised model trained on 61.7% data samples.
doi:10.1137/1.9781611972832.42 dblp:conf/sdm/DengHJLLW13 fatcat:kbw6mzcw7fgmlea66b3acphcta