Tracking topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery

Harakawa, Ryosuke; Ogawa, Takahiro; Haseyama, Miki

doi:10.1007/s11042-017-5404-4

Cited by 5 publications

(5 citation statements)

References 52 publications

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“…First, we generate clustering results focusing on the semantics of tweets. There have been reports that the Louvain method [23] works well for multimedia content clustering [56]- [58]. As in these reports, we apply the Louvain method [23] to semantic networks G k α = (V k α , E k α ).…”

Section: B Generation Of Multiple Clustering Resultsmentioning

confidence: 99%

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

et al. 2019

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Although Twitter has become an important source of information, the number of accessible tweets is too large for users to easily find their desired information. To overcome this difficulty, a method for tweet clustering is proposed in this paper. Inspired by the reports that network representation is useful for multimedia content analysis including clustering, a network-based approach is employed. Specifically, a consensus clustering method for tweet networks that represent relationships among the tweets' semantics and sentiment are newly derived. The proposed method integrates multiple clustering results obtained by applying successful clustering methods to the tweet networks. By integrating complementary clustering results obtained based on semantic and sentiment features, the accurate clustering of tweets becomes feasible. The contribution of this work can be found in the utilization of the features, which differs from existing network-based consensus clustering methods that target only the network structure. Experimental results for a real-world Twitter dataset, which includes 65 553 tweets of 25 datasets, verify the effectiveness of the proposed method.

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Section: B Generation Of Multiple Clustering Resultsmentioning

confidence: 99%

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

et al. 2019

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“…Compared to the eld of textual information, there are very few studies on the evolution of video topics. Ryosuke et al proposed a topic evolution tracking algorithm based on salient keyword matching with consideration of semantic broadness for web videos, and tracked the topic evolution from the hierarchy of video groups [42], which can make users understand the topic evolution over time to nd web videos they are interested in, but the calculation cost is high and the amount of data is large. Cao et al presented an e cient algorithm based on salient trajectory extraction on a topic evolution link graph, which considers tag and visual similarities [43].…”

Section: Topic Evolutionmentioning

confidence: 99%

Multimodality Fusion based Topic Detection and Evolution Analysis of Web Videos

Zhu,

Wei,

Wei

et al. 2024

Preprint

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Due to the prevalence of video social media and the increase of user generated content, the Internet is filled with a large amount of unstructured data. Videos often contain multimodal data such as title, tags, images and audios. Therefore, fusion of multimodal features is a valid way for video topic detection. The titles and tags of videos are short and sparse, and they are high level semantics, whereas the audio and images of videos are low level semantics. It is not suitable to represent a video by directly fusing these features. To address the issue, an effective multimodal fusion method based on the transformer model is proposed for detecting video topics. First, video data is crawled from Bilibili platform, and the titles, tags and descriptions of videos are processed by deleting invalid symbols and null values. The audios are converted to text and texts are recognized from video covers. Second, the transformer-based model is applied to fuse the three forms of text from different modalities to represent videos with multi-dimensional vectors. Then the HDBSCAN and hierarchical clustering (HC) are compared by Silhouette coefficient when clustering videos for topic detection. In addition, we compare video topic clustering under multimodal and single-modal. Finally, the intensity and content evolution of video topics over time are analyzed in the paper. Experimental results with the real data collected from Bilibili verify the effectiveness of the proposed method for video topic detection and evolution.

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“…Using G, we detect tweet communities with similar topics. Following the reports that the Louvain method [32] works well for multimedia content clustering [9], [11], [33], [34], we apply the Louvain method [32] to G. The Louvain method is based on a quality measure of community detection results called modularity [35]. The modularity Q is defined as…”

Section: B Construction Of Community Networkmentioning

confidence: 99%

“…Using \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$G$ \end{document} , we detect tweet communities with similar topics. Following the reports that the Louvain method [32] works well for multimedia content clustering [9] , [11] , [33] , [34] , we apply the Louvain method [32] to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$G$ \end{document} . The Louvain method is based on a quality measure of community detection results called modularity [35] .…”

Section: Ranking Of Tweet Communitiesmentioning

confidence: 99%

“…One of the effective solutions to this problem is the visualization of an overview of a large amount of content [7] – [12] . Qian et al [7] explained that it is very time-consuming for users who are not familiar with a topic to browse large amounts of content and quickly gain a general understanding of it; therefore, it is important to automatically mine multiview opinions on the target topic.…”

Section: Introductionmentioning

confidence: 99%

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Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan

Harakawa

Iwahashi

2021

IEEE Trans. Comput. Soc. Syst.

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This article presents a method that detects tweet communities with similar topics and ranks the communities by importance measures. By identifying the tweet communities that have high importance measures, it is possible for users to easily find important information about the coronavirus disease (COVID-19). Specifically, we first construct a community network, whose nodes are tweet communities obtained by applying a community detection method to a tweet network. The community network is constructed based on textual similarities between tweet communities and sizes of tweet communities. Second, we apply algorithms for calculating centrality to the community network. Because the obtained centrality is based on tweet community sizes as well, we call it the importance measure in distinction to conventional centrality. The importance measure can simultaneously evaluate the importance of topics in the entire data set and occupancy (or dominance) of tweet communities in the network structure. We conducted experiments by collecting Japanese tweets about COVID-19 from March 1, 2020 to May 15, 2020. The results show that the proposed method is able to extract keywords that have a high correlation with the number of people infected with COVID-19 in Japan. Because users can browse the keywords from a small number of central tweet communities, quick and easy understanding of important information becomes feasible.

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Tracking topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery

Cited by 5 publications

References 52 publications

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

Multimodality Fusion based Topic Detection and Evolution Analysis of Web Videos

Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan

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