A Novel Hot Topic Detection Framework With Integration of Image and Short Text Information From Twitter

Zhang, Chengde; Lu, S Z; Zhang, Chengming; Xiao, Xia; Wang, Qian; Gao, Chen

doi:10.1109/access.2018.2886366

Cited by 20 publications

(8 citation statements)

References 23 publications

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“…Kireyev et al proposed a modified LDA method to identify topics from tweets [35]. Zhang et al proposed an approach to obtaining hot topics from Twitter [36]. Furthermore, Weng et al merged tweets into a single document and applied the original LDA method [37].…”

Section: 1mentioning

confidence: 99%

Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

Sakai

Matsumoto

2023

IEEJ Journal IA

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In this study, we established a topic selection method that recommends topics that are interesting and unfamiliar to users. To achieve this aim, we used conceptual distance to identify topics that were unfamiliar to users and improved the accuracy of this method by removing conceptually similar words. Many words used in conversations are excluded in the dictionaries and thesauruses. Thus, we developed a model for conceptual distance measurement using machine learning to measure conceptual distances even for such words. By conducting the subject experiments, we confirmed that the established system recommends topics a user is interested in but unfamiliar with compared with the baseline method developed in a previous research.

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Section: 1mentioning

confidence: 99%

Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

Sakai

Matsumoto

2023

IEEJ Journal IA

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“…Latent Dirichlet allocation (LDA) is an unsupervised machine learning technique based on a probabilistic graph model [29]. It can discover potential topics in large-scale document sets, and it works well in text mining, especially short text processing [30]. Supposing the set of documents is D, the set of topics is T, and the vocabulary in the document is denoted by w, then the relationship between the topic, document, and vocabulary of LDA can be expressed by the following formula [31]:…”

Section: Foundation Theory For Ldamentioning

confidence: 99%

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

et al. 2021

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Tracking scientific and technological (S&T) research hotspots can help scholars to grasp the status of current research and develop regular patterns in the field over time. It contributes to the generation of new ideas and plays an important role in promoting the writing of scientific research projects and scientific papers. Patents are important S&T resources, which can reflect the development status of the field. In this paper, we use topic modeling, topic intensity, and evolutionary computing models to discover research hotspots and development trends in the field of blockchain patents. First, we propose a time-based dynamic latent Dirichlet allocation (TDLDA) modeling method based on a probabilistic graph model and knowledge representation learning for patent text mining. Second, we present a computational model, topic intensity (TI), that expresses the topic strength and evolution. Finally, the point-wise mutual information (PMI) value is used to evaluate topic quality. We obtain 20 hot topics through TDLDA experiments and rank them according to the strength calculation model. The topic evolution model is used to analyze the topic evolution trend from the perspectives of rising, falling, and stable. From the experiments we found that 8 topics showed an upward trend, 6 topics showed a downward trend, and 6 topics became stable or fluctuated. Compared with the baseline method, TDLDA can have the best effect when K is 40 or less. TDLDA is an effective topic model that can extract hot topics and evolution trends of blockchain patent texts, which helps researchers to more accurately grasp the research direction and improves the quality of project application and paper writing in the blockchain technology domain.

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“…In the traditional text field, a large number of scholars focus on topic detection and tracking based on news articles. A lot of outstanding contributions have been made and fruitful results have been achieved (e.g., [2], [7]- [16]). With the extensive application of pictures and videos, topic detection and tracking based on multimedia data has gradually become popular.…”

Section: B Tdtmentioning

confidence: 99%

A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual Information

Zhang

Jin

Xiao³

et al. 2020

IEEE Access

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The high speed and low latency of 5G mobile network have accelerated the speed and amount of information transmission. Web video is likely to become the main mode of news production and dissemination in the future for its richer information and more convenient dissemination, which will subvert the traditional mode of event mining. Therefore, event mining based on web videos has become a new research hotspot. However, web videos are vulnerable to video editing, lighting, shooting perspective and shooting angle, and other factors, resulting in the inaccurate visual similarity detection problem. Generally speaking, effectively integrating humungous volumes of cross-model information would give a great help. However, web videos are described with few terms, and thus sparse text information becomes a challenge for cross-model information combination. To address this issue, this paper proposes a new collaborative optimization framework with the combination of inaccurate visual similarity detection information and sparse textual information. This framework is composed of three steps. After obtaining the statistics of the distribution characteristics of each word in all Near-Duplicate Keyframes (NDKs), the high-level semantic cross-correlation between NDKs is first mined with the help of textual features, forming a new set of semantic relevant NDKs with different visual expressions. Next, textual distribution features are enriched through finding more semantically related words by the new NDK set with various forms of visual expressions, solving the sparse distribution problem for each word in all NDKs. Finally, Multiple Correspondence Analysis (MCA) is used to mine the events. Experimental results with a large number of real data demonstrate that the proposed model outperforms the existing methods for web video event mining. INDEX TERMS Event mining, web video, near-duplicate keyframes (NDKs), topic detection and tracking (TDT).

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A Novel Hot Topic Detection Framework With Integration of Image and Short Text Information From Twitter

Cited by 20 publications

References 23 publications

Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

Technology Hotspot Tracking: Topic Discovery and Evolution of China’s Blockchain Patents Based on a Dynamic LDA Model

A Novel Collaborative Optimization Framework for Web Video Event Mining Based on the Combination of Inaccurate Visual Similarity Detection Information and Sparse Textual Information

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