Detecting TV Program Highlight Scenes Using Twitter Data Classified by Twitter User Behavior and Evaluating It to Soccer Game TV Programs

Tessai HAYAMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.4   pp.917-924
Publication Date: 2018/04/01
Publicized: 2018/01/19
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016IIP0020
Type of Manuscript: Special Section PAPER (Special Section on Intelligent Information and Communication Technology and its Applications to Creative Activity Support)
Category: Datamining Technologies
highlight-scene detection,  TV digest generation,  Twitter data,  burst detection,  

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This paper presents a novel TV event detection method for automatically generating TV program digests by using Twitter data. Previous studies of TV program digest generation based on Twitter data have developed TV event detection methods that analyze the frequency time series of tweets that users made while watching a given TV program; however, in most of the previous studies, differences in how Twitter is used, e.g., sharing information versus conversing, have not been taken into consideration. Since these different types of Twitter data are lumped together into one category, it is difficult to detect highlight scenes of TV programs and correctly extract their content from the Twitter data. Therefore, this paper presents a highlight scene detection method to automatically generate TV program digests for TV programs based on Twitter data classified by Twitter user behavior. To confirm the effectiveness of the proposed method, experiments using 49 soccer game TV programs were conducted.