Research progress and trend of leader member exchange based on social complex network and latent dirichlet allocation topic model

2020 ◽  
Author(s):  
Zhang chunyang ◽  
Ding kun ◽  
Zhang chunbo ◽  
Zhang li
Keyword(s):  
Complex Network ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Research Progress ◽  
Leader Member Exchange ◽  
Member Exchange ◽  
Dirichlet Allocation

scholarly journals Intelligent radar software defect classification approach based on the latent Dirichlet allocation topic model

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xi Liu ◽  
Yongfeng Yin ◽  
Haifeng Li ◽  
Jiabin Chen ◽  
Chang Liu ◽  
...  
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Recall Rate ◽  
Defect Classification ◽  
Software Defects ◽  
Classification Approach ◽  
Software Defect ◽  
Model Combining ◽  
Dirichlet Allocation

AbstractExisting software intelligent defect classification approaches do not consider radar characters and prior statistics information. Thus, when applying these appaoraches into radar software testing and validation, the precision rate and recall rate of defect classification are poor and have effect on the reuse effectiveness of software defects. To solve this problem, a new intelligent defect classification approach based on the latent Dirichlet allocation (LDA) topic model is proposed for radar software in this paper. The proposed approach includes the defect text segmentation algorithm based on the dictionary of radar domain, the modified LDA model combining radar software requirement, and the top acquisition and classification approach of radar software defect based on the modified LDA model. The proposed approach is applied on the typical radar software defects to validate the effectiveness and applicability. The application results illustrate that the prediction precison rate and recall rate of the poposed approach are improved up to 15 ~ 20% compared with the other defect classification approaches. Thus, the proposed approach can be applied in the segmentation and classification of radar software defects effectively to improve the identifying adequacy of the defects in radar software.

The surveillance of a supreme audit institution on related party transactions

2020 ◽  
Vol 32 (4) ◽  
pp. 577-603
Author(s):  
Gustavo Cesário ◽  
Ricardo Lopes Cardoso ◽  
Renato Santos Aranha
Keyword(s):  
Public Sector ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Conflicts Of Interest ◽  
International Standards ◽  
Content Type ◽  
Related Party Transactions ◽  
The Public ◽  
Audit Reports ◽  
Dirichlet Allocation

PurposeThis paper aims to analyse how the supreme audit institution (SAI) monitors related party transactions (RPTs) in the Brazilian public sector. It considers definitions and disclosure policies of RPTs by international accounting and auditing standards and their evolution since 1980.Design/methodology/approachBased on archival research on international standards and using an interpretive approach, the authors investigated definitions and disclosure policies. Using a topic model based on latent Dirichlet allocation, the authors performed a content analysis on over 59,000 SAI decisions to assess how the SAI monitors RPTs.FindingsThe SAI investigates nepotism (a kind of RPT) and conflicts of interest up to eight times more frequently than related parties. Brazilian laws prevent nepotism and conflicts of interest, but not RPTs in general. Indeed, Brazilian public-sector accounting standards have not converged towards IPSAS 20, and ISSAI 1550 does not adjust auditing procedures to suit the public sector.Research limitations/implicationsThe SAI follows a legalistic auditing approach, indicating a need for regulation of related public-sector parties to improve surveillance. In addition to Brazil, other code law countries might face similar circumstances.Originality/valuePublic-sector RPTs are an under-investigated field, calling for attention by academics and standard-setters. Text mining and latent Dirichlet allocation, while mature techniques, are underexplored in accounting and auditing studies. Additionally, the Python script created to analyse the audit reports is available at Mendeley Data and may be used to perform similar analyses with minor adaptations.

Linguistic and Personological Features of the Doka & Martin Grieving Style Continuum

2021 ◽  
Author(s):  
James Geisler ◽  
Cass Dykeman
Keyword(s):  
Corpus Linguistics ◽  
Language Use ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Psychological Processes ◽  
Clinical Vignettes ◽  
Present Tense ◽  
Dirichlet Allocation

While there is extensive research on the adaptive grief styles developed by Doka and Martin, this study is the first of its kind to explore the language used among each style of grief. This study used clinical vignettes from a variety of sources on instrumental and intuitive grieving in an attempt to decipher the language use across various linguistic and psychological processes. Following this analysis, latent Dirichlet allocation (LDA) was used fitting a two-topic model to analyze differences between topics while additionally performing a supervised LDA analysis. The strongest data from this study relate to intuitive grief, which found a higher use of present-tense language in comparison to the instrumental grief style. In addition, results found that the language used by intuitive grievers is slightly more distinguishable than that of its instrumental counterpart. Several implications for counseling and research were developed in response to these findings.Keywords: corpus linguistics, grieving, instrumental grieving, intuitive grieving, LIWC, latent Dirichlet allocation (LDA).

scholarly journals Predicting Component Failures Using Latent Dirichlet Allocation

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Hailin Liu ◽  
Ling Xu ◽  
Mengning Yang ◽  
Meng Yan ◽  
Xiaohong Zhang
Keyword(s):  
Latent Dirichlet Allocation ◽  
Semantic Information ◽  
Topic Model ◽  
Source Code ◽  
Statistical Correlation ◽  
Similarity Matrix ◽  
Component Failure ◽  
Program Behavior ◽  
Component Failures ◽  
Dirichlet Allocation

Latent Dirichlet Allocation (LDA) is a statistical topic model that has been widely used to abstract semantic information from software source code. Failure refers to an observable error in the program behavior. This work investigates whether semantic information and failures recorded in the history can be used to predict component failures. We use LDA to abstract topics from source code and a new metric (topic failure density) is proposed by mapping failures to these topics. Exploring the basic information of topics from neighboring versions of a system, we obtain a similarity matrix. Multiply the Topic Failure Density (TFD) by the similarity matrix to get the TFD of the next version. The prediction results achieve an average 77.8% agreement with the real failures by considering the top 3 and last 3 components descending ordered by the number of failures. We use the Spearman coefficient to measure the statistical correlation between the actual and estimated failure rate. The validation results range from 0.5342 to 0.8337 which beats the similar method. It suggests that our predictor based on similarity of topics does a fine job of component failure prediction.

Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation

2018 ◽  
Vol 18 (1) ◽  
pp. 101-117 ◽  
Author(s):  
Carlo Schwarz
Keyword(s):  
Machine Learning ◽  
Probability Distribution ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Topic Models ◽  
Text Documents ◽  
Text Data ◽  
Dirichlet Allocation

In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.

scholarly journals An Indirected Recommendation Model for Chinese Microblog

2016 ◽  
Vol 16 (2) ◽  
pp. 148-159
Author(s):  
Jianyong Duan ◽  
Zheng Dong ◽  
Mei Zhang
Keyword(s):  
Comparative Analysis ◽  
Information Sharing ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Experimental Results ◽  
User Interests ◽  
Recommendation Algorithms ◽  
Dirichlet Allocation

Abstract Microblog is a browser-based platform for web user’s information sharing and communication. With the rapidly increasing of microblog population, its recommendation function becomes necessary. This paper proposes the recommendation by the Latent Dirichlet Allocation topic model, which combines the user interests into the model to meet their needs. We also conduct a comparative analysis between indirect and direct recommendation algorithms. The experimental results show that the indirect recommendation is more effective for the micro-blog recommendation.

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