Conceptualization topic modeling

Tang, Yi-Kun; Mao, Xian-Ling; Huang, Heyan; Shi, Xuewen; Wen, Gongjian

doi:10.1007/s11042-017-5145-4

Cited by 17 publications

(3 citation statements)

References 24 publications

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“…Although the proposed method takes the dependency information among words and semantics associations in the text into account, it abstracts away from the influence of time on the sentiment and topic. The following study will consider the use of the existing inference algorithms such as the collapse of Gibbs sampling 45,46 and variational Bayesian 51–57 to detect dynamic topic and sentiment. We also consider developing a more flexible and efficient inference algorithm to accommodate the training requirements of the large‐scale corpus.…”

Section: Discussionmentioning

confidence: 99%

Topic sentiment analysis using words embeddings dependency in edge social system

Xie

Zhang

et al. 2019

Trans Emerging Tel Tech

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The topic sentiment analysis is really fundamental in detecting potential cyber threats and cyber attacks in edge social systems. We can detect potential cyber threats and cyber attacks by identifying the sentiment orientation and topics of public opinion information in the edge social system. Topic sentiment joint model is an extended model, which aims to deal with the problem of detecting sentiments and topics simultaneously from the online comment. Most existing topic sentiment joint models ignore the dependency among words so that they lose rich semantic information and the resulting distribution might be not very satisfactory. In this paper, we propose a novel topic sentiment joint model with word embeddings dependency based on recurrent neural network. The model introduces the dependency among word embedding and delivers topic information and sentiment information of words by a recurrent neural network. It fully extends the semantic information and redefines the topic sentiment‐word distribution. Moreover, we obtain more accurate topic detection and sentiment analysis. Experimental results on online review data set show that the proposed model significantly improves the sentiment classification accuracy and achieved better topic detection compared with previous methods.

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Section: Discussionmentioning

confidence: 99%

Topic sentiment analysis using words embeddings dependency in edge social system

Xie

Zhang

et al. 2019

Trans Emerging Tel Tech

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“…Applications of Probase include semantic search [94][95][96], understanding Web tables [97], question answering [90], short text understanding [98,99] and classification [100], topic modelling [101], open directory based text classification [102], and learning entity and concept representation [103].…”

Section: Characteristicsmentioning

confidence: 99%

A survey on automatically constructed universal knowledge bases

Hossain

Salam

Schwitter

2020

Journal of Information Science

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A universal knowledge base can be defined as a domain-independent ontology containing instances. Ontologies define the concepts and relations among these concepts and are used to represent a domain of interest. These universal knowledge bases are the elementary units for automated reasoning on the Semantic Web. The Semantic Web is an extension of the World Wide Web which facilitates software agents to share content beyond the limitations of applications and websites. This survey focuses on the most prominent automatically constructed universal knowledge bases including KnowItAll, DBpedia, YAGO, NELL, Probase, BabelNet and Knowledge Vault. We take a closer look at how these knowledge bases are built, in particular at the information extraction and taxonomy generation process and investigate how they are used in practical applications. Due to quality concerns, the most successful and widely employed knowledge bases are manually constructed to maintain high quality, but they suffer from low coverage, high assembly and quality assurance cost. On the contrary, automatic approaches for building knowledge bases try to overcome these drawbacks. Although it is strenuous to achieve the same level of quality as for manual knowledge bases, we found that the surveyed automatically constructed knowledge bases have shown promising results and are useful for many real-world applications.

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“…This is also why topic models based on word embeddings are, in this context, a superior solution to algorithms that use concept databases, such asTang et al (2018), or algorithms that model concepts simply as latent variables, asEl-Arini, Fox, and Guestrin (2012).…”

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confidence: 99%

Mixing syntagmatic and paradigmatic information for concept detection

Chartrand,

Bouguessa

2019

Preprint

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In the last decades, philosophers have begun using empirical data for conceptual analysis, but corpus-based conceptual analysis has so far failed to develop, in part because of the absence of reliable methods to automatically detect concepts in textual data. Previous attempts have shown that topic models can constitute efficient concept detection heuristics, but while they leverage the syntagmatic relations in a corpus, they fail to exploit paradigmatic relations, and thus probably fail to model concepts accurately. In this article, we show that using a topic model that models concepts on a space of word embeddings (Hu and Tsujii, 2016) can lead to significant increases in concept detection performance, as well as enable the target concept to be expressed in more flexible ways using word vectors.

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Conceptualization topic modeling

Cited by 17 publications

References 24 publications

Topic sentiment analysis using words embeddings dependency in edge social system

Topic sentiment analysis using words embeddings dependency in edge social system

A survey on automatically constructed universal knowledge bases

Mixing syntagmatic and paradigmatic information for concept detection

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