Extracting Hierarchical Structure of Web Video Groups Based on Sentiment-Aware Signed Network Analysis

Abstract

Sentiment in multimedia contents has an influence on their topics, since multimedia contents are tools for social media users to convey their sentiment. Performance of applications such as retrieval and recommendation will be improved if sentiment in multimedia contents can be estimated; however, there have been few works in which such applications were realized by utilizing sentiment analysis. In this paper, a novel method for extracting the hierarchical structure of Web video groups based on sentiment-aware signed network analysis is presented to realize Web video retrieval. First, the proposed method estimates latent links between Web videos by using multimodal features of contents and sentiment features obtained from texts attached to Web videos. Thus, our method enables construction of a signed network that reflects not only similarities but also positive and negative relations between topics of Web videos. Moreover, an algorithm to optimize a modularity-based measure, which can adaptively adjust the balance between positive and negative edges, was newly developed. This algorithm detects Web video groups with similar topics at multiple abstraction levels; thus, successful extraction of the hierarchical structure becomes feasible. By providing the hierarchical structure, users can obtain an overview of many Web videos and it becomes feasible to successfully retrieve the desired Web videos. Results of experiments using a new benchmark dataset, YouTube-8M, validate the contributions of this paper, i.e. , 1) the first attempt to utilize sentiment analysis for Web video grouping and 2) a novel algorithm for analyzing a weighted signed network derived from sentiment and multimodal features.