Topic Analysis of Web User Behavior Using LDA Model on Proxy Logs

Fujimoto, Hiroshi; Etoh, Minoru; Kinno, Akira; Akinaga, Yoshikazu

doi:10.1007/978-3-642-20841-6_43

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…It is a powerful model for analyzing massive sets of text data. Many works on topic detection have used LDA (Lau et al, 2012) (Fujimoto et al, 2011) (Keane et al, 2015).…”

Section: Supplementary Experiments Using Ldamentioning

confidence: 99%

“…Our research is related to trend or topic detection (Mathioudakis and Koudas, 2010) (Glance et al, 2004) (Weng and Lee, 2011) (Wang et al, 2013) (Mochizuki and Shibano, 2014) (Lau et al, 2012) (Fujimoto et al, 2011) (Keane et al, 2015) and buzzword extraction (Nakajima et al, 2012) studies. Many of these studies aim to extract popular topics or buzzwords from a large number of texts in consumergenerated medias (CGM), such as Weblog articles or tweets, but we analyze closed-caption text from the mass media.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Topics Popular in the Recent Past from a Closed Caption TV Corpus as a Categorized Chronicle Data

Mochizuki

Shibano

2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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In this paper, we propose a method for extracting topics we were interested in over the course of the past 28 months from a closed-caption TV corpus. Each TV program is assigned one of the following genres: drama, informational or tabloid-style program, music, movie, culture, news, variety, welfare, or sport. We focus on informational/tabloid-style programs, dramas and news in this paper. Using our method, we extracted bigrams that formed part of the signature phrase of a heroine and the name of a hero in a popular drama, as well as recent world, domestic, showbiz, and so on news. Experimental evaluations show that our simple method is as useful as the LDA model for topic detection, and our closed-caption TV corpus has the potential value to act as a rich, categorized chronicle for our culture and social life.

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“…It is a powerful model for analyzing massive sets of text data. Many works on topic detection have used LDA (Lau et al, 2012) (Fujimoto et al, 2011) (Keane et al, 2015).…”

Section: Supplementary Experiments Using Ldamentioning

confidence: 99%