Modeling on micro-blog topic detection based on semantic dependency

Ruan, Dongru; Jia, Han; Dang, Yi; Zhang, Shanshan; Gao, Kai

doi:10.1109/icmic.2017.8321571

Cited by 2 publications

(2 citation statements)

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“…Te words can be correlated through similarity, cooccurrence, proximity, and a subject-predicate structure [33,34]. Each document is represented by a vector with dimensions corresponding to each term in the vocabulary and valued with the weights of the terms [35].…”

Section: Complexitymentioning

confidence: 99%

“…Topic modeling algorithms are a multiperspective technique applied to discover the semantics in a corpus and to group extracted word patterns into topics. Tese algorithms generate a representation of the word meanings based on statistical and probabilistic analyses of the words [24,28,33]. Te algebraic perspective was initially followed to reduce dimensionality, as the original matrix is decomposed into a matrix of factors.…”

Section: Topic Modeling Algorithmsmentioning

confidence: 99%

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Conceptualizing Discussions on the Dark Web: An Empirical Topic Modeling Approach

Basheer,

Alkhatib

2024

Complexity

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Social networks on the Internet have become a home that attracts all types of human thinking to exchange knowledge and ideas and share businesses. On the other hand, it has also become a source for researchers to analyze this knowledge and frame it in patterns that define types of thoughts circulating on these networks and representing the communities around them. In particular, some social networks on the Dark Web attract a special kind of thinking centered around the malicious and illegal activities disseminated on websites and marketplaces on the Dark Web. These networks involve discussions to exchange and discourse information, tips, and advice on performing such business. Studying social networks on the Dark Web is still in its infancy. In this paper, we present a methodology for analyzing the content of social networks on the Dark Web using topic modeling methods. We demonstrate the needed stages for the topic modeling process, beginning with data preprocessing and feature extraction to topic modeling algorithms. We utilize and discuss the following four topic models: LDA, CTM, PAM, and PTM. We discuss the following four topic coherence measures as evaluation metrics: UMass, UCI, CNPMI, and CV, demonstrating the selection of the best number of topics for each model according to the most coherent produced topics. Furthermore, we discuss the limitations, challenges, and future work. Our proposed approach highlights the ability to discover the latent thematic patterns in conversations and messages in the common language used in social networks on the Dark Web, constructing topics as groups of terms and their associations. This paper provides researchers with a leading methodology for analyzing thought patterns on the Dark Web.

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Section: Complexitymentioning

confidence: 99%

Section: Topic Modeling Algorithmsmentioning

confidence: 99%