scholarly journals Marketing Improvement of Chinese Original Picture Books From Dissatisfaction Evaluation - Text Mining Based on LDA Model

2021 ◽  
Vol 123 ◽  
pp. 01005
Author(s):  
Ziyan Tang ◽  
Wen Yuan
Keyword(s):  
Cultural Identity ◽  
Picture Books ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Traditional Culture ◽  
Integrated Marketing ◽  
Bottom Line ◽  
Content Creation ◽  
Original Picture ◽  
Picture Content

Chinese original picture books play an important role in inheriting traditional culture and forming cultural identity, which is very important for children. We analyzes the dissatisfaction evaluation of Chinese original picture books by using the topic model of Latent Dirichlet Allocation (LDA). It is found that the dissatisfaction of consumers mainly focuses on smell, AR function, preaching, quality, picture, content, painting style and so on. In the future, we should take quality as the bottom line, focus on the content creation, and carry out integrated marketing through social media.

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

2021 ◽  
pp. 0887302X2199826
Author(s):  
Muzhen Li ◽  
Li Zhao
Keyword(s):  
Latent Dirichlet Allocation ◽  
Corporate Sustainability ◽  
Computer Assisted ◽  
Competitive Advantages ◽  
Bottom Line ◽  
Product Categories ◽  
Sustainability Strategies ◽  
Line Theory ◽  
Sustainability Practices ◽  
Sustainability Theory

Nowadays, more fashion companies have started to adopt various sustainability practices and communicate these practices through their annual public CSR reports. In this study, we aim to provide a holistic perspective of fashion companies’ sustainable development and investigate the sustainability practices of global fashion companies. A total of 181 CSR reports from 29 fashion companies were collected. A Dictionary approach text classification method, combined with Latent Dirichlet Allocation (LDA), a computer-assisted topic modeling algorithm, was implemented to detect and summarize the themes and keywords of detailed practices disclosed in CSR reports. The findings identified 12 main sustainability practices themes based on the triple bottom line theory and the moral responsibility of corporate sustainability theory. In general, waste management and human rights are the most frequently mentioned themes. The findings also suggest that global fashion companies adopted different sustainability strategies based on their product categories and competitive advantages.

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.

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

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Jinli Wang ◽  
Yong Fan ◽  
Hui Zhang ◽  
Libo Feng
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Graph Model ◽  
Representation Learning ◽  
Research Direction ◽  
Calculation Model ◽  
Topic Evolution ◽  
Blockchain Technology ◽  
The Status ◽  
Research Hotspots

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.

A true study of a benevolent state. S.N. Baburin. Moral State. Russian view on the values of constitutionalism

2021 ◽  
pp. 197
Author(s):  
Orlin Zagorov
Keyword(s):  
Cultural Identity ◽  
Traditional Culture ◽  
The State ◽  
Internal Quality ◽  
Orthodox Christianity ◽  
Solid Foundation ◽  
The Revolution

This article is the author's reflections on the problems of humanism, morality, and traditional culture in connection with the concept of a Moral State put forward by Professor S.N. Baburin. The role of the spirituality of the Slavic peoples and their contribution to the strengthening of European cultural identity is considered. The author argues the importance of the conclusion that the virtue of the state as its internal quality in itself turns the state into a guarantor of virtue as a universal value and the validity of the thesis that the values of both Orthodox Christianity and Slavic spirituality represent a solid foundation of a Moral State. The author sees in the Moral State a mechanism for the harmonious combination of the spirit of the revolution with the revolution of the spirit.

scholarly journals An exploration of text mining of narrative reports of injury incidents to assess risk

2018 ◽  
Vol 251 ◽  
pp. 06020 ◽  
Author(s):  
David Passmore ◽  
Chungil Chae ◽  
Yulia Kustikova ◽  
Rose Baker ◽  
Jeong-Ha Yim
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Surface Mining ◽  
Modeling Processes ◽  
Free Text ◽  
Text Data ◽  
Injury Occurrence ◽  
The Usa ◽  
Musculoskeletal Systems ◽  
Topic Mining

A topic model was explored using unsupervised machine learning to summarized free-text narrative reports of 77,215 injuries that occurred in coal mines in the USA between 2000 and 2015. Latent Dirichlet Allocation modeling processes identified six topics from the free-text data. One topic, a theme describing primarily injury incidents resulting in strains and sprains of musculoskeletal systems, revealed differences in topic emphasis by the location of the mine property at which injuries occurred, the degree of injury, and the year of injury occurrence. Text narratives clustered around this topic refer most frequently to surface or other locations rather than underground locations that resulted in disability and that, also, increased secularly over time. The modeling success enjoyed in this exploratory effort suggests that additional topic mining of these injury text narratives is justified, especially using a broad set of covariates to explain variations in topic emphasis and for comparison of surface mining injuries with injuries occurring during site preparation for construction.

scholarly journals Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

2016 ◽  
Author(s):  
Timothy N. Rubin ◽  
Oluwasanmi Koyejo ◽  
Krzysztof J. Gorgolewski ◽  
Michael N. Jones ◽  
Russell A. Poldrack ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Brain Activity ◽  
Human Cognition ◽  
Brain Images ◽  
Whole Brain ◽  
Context Sensitive ◽  
Cognitive States ◽  
Small Set

AbstractA central goal of cognitive neuroscience is to decode human brain activity--i.e., to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive--i.e., capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a Bayesian decoding framework based on a novel topic model---Generalized Correspondence Latent Dirichlet Allocation---that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text--enabling researchers, for the first time, to generative quantitative, context-sensitive interpretations of whole-brain patterns of brain activity.

Folclor și identitate

2019 ◽  
Vol 17 ◽  
pp. 183-199
Author(s):  
Sabina Ispas
Keyword(s):  
Cultural Identity ◽  
Traditional Culture ◽  
Industrial Society ◽  
Access To Information ◽  
Traditional System ◽  
National Heritage ◽  
Post Industrial ◽  
Over Time ◽  
Language Literature ◽  
Specific Identity

The folklore phenomenon (the deep, oral, popular, traditional culture) is represented by all the creations of a community that is based on tradition, are expressed by a group of individuals and recognized as reflections of its expectations to the extent that it represents its social and cultural identity. This is, in fact, a sum of local, village and city identities in which “individual identities” are incorporated. Through it, the fusion between territory, language and people is obtained, which is legitimized “through a genealogy and a space conceptualized as such”. We belong to a world in which access to information is open to all. In this context, in order to find yourself, you must define and assume your own identity. Such a complicated and responsible process cannot be undertaken without reference to the traditional system of norms, expressed in that large segment of the culture that is folklore. A Europe of nations cannot be achieved without knowing and understanding the system of values to which they have appealed throughout the entire period of their definition. Folklore is a fundamental landmark for the man of the post-industrial society who is in search of the self. Along with the scholarly culture, to which it is complementary, folklore contributes to the realization of the universal, European, national heritage. Forms of expression of folklore, musical, literary or choreic texts, ritual practices, beliefs, the dominant religion of the group, etc. cooperates for the purpose of forming this identity. The standards and values are transmitted orally, by imitation or other means. Folklore includes, inter alia, phenomena of language, literature, music, dance, games, mythology, rituals, customs, crafts, architecture and other arts. The present study highlights, synthetically and systematically, the main traditions created and developed over time by the Romanian people, traditions that define it and give it a specific identity.

scholarly journals The Voice of Drug Consumers: Online Textual Review Analysis Using Structural Topic Model

2020 ◽  
Vol 17 (10) ◽  
pp. 3648 ◽  
Author(s):  
Lifeng He ◽  
Dongmei Han ◽  
Xiaohang Zhou ◽  
Zheng Qu
Keyword(s):  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Web Based ◽  
Pharmaceutical Manufacturers ◽  
Textual Data ◽  
Consumer Dissatisfaction ◽  
The Voice ◽  
Document Level ◽  
Structural Topic Model ◽  
Over Time

Many web-based pharmaceutical e-commerce platforms allow consumers to post open-ended textual reviews based on their purchase experiences. Understanding the true voice of consumers by analyzing such a large amount of user-generated content is of great significance to pharmaceutical manufacturers and e-commerce websites. The aim of this paper is to automatically extract hidden topics from web-based drug reviews using the structural topic model (STM) to examine consumers’ concerns when they buy drugs online. The STM is a probabilistic extension of Latent Dirichlet Allocation (LDA), which allows the consolidation of document-level covariates. This innovation allows us to capture consumer dissatisfaction along with their dynamics over time. We extract 12 topics, and five of them are negative topics representing consumer dissatisfaction, whose appearances in the negative reviews are substantially higher than those in the positive reviews. We also come to the conclusion that the prevalence of these five negative topics has not decreased over time. Furthermore, our results reveal that the prevalence of price-related topics has decreased significantly in positive reviews, which indicates that low-price strategies are becoming less attractive to customers. To the best of our knowledge, our work is the first study using STM to analyze the unstructured textual data of drug reviews, which enhances the understanding of the aspects of drug consumer concerns and contributes to the research of pharmaceutical e-commerce literature.

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