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To solve the cold start problem caused by user interest sparsity in cross-area project recommendation, this paper proposes a recommendation method on mixed interest topic model PA-LDA. PA-LDA uses the P-LDA module, which generates the interest topic distribution to target project by mining users′ historical behavior data. Then P-LDA employs conduct parameter estimation to build model by the interaction between the topics and the content words, which helps to measure the users′ interest on the target project. PA-LDA uses A-LDA module to measure the area interest on the target project. PA-LDA employs top-k method to recommend the target project based on the result of the two mixed interest measurements. The effectiveness and efficiency of our method are verified by experiments on two real data sets EdX and GCSE. The research can effectively explain the principles of effect on recommendation by user interest and domain interest. It also realizes the interest feature capture in multi-dimensional area recommendation, which improves the adaptability and accuracy of recommendation. [ABSTRACT FROM AUTHOR]

Topic models are often used in modeling unstructured corpora and discrete data to extract the latent topic. As topics are generally expressed in the form of word lists, it is usually difficult for users to understand the meanings of topics, especially when users lack knowledge in the subject area. Although manually labeling topics can generate more explanatory and easily understandable topic labels, the cost is too high for the method to be feasible. Therefore, research on automatic labeling of topic discovered provides solutions to the problem. Firstly, the currently most popular technique, latent Dirichlet allocation (LDA), is elaborated and analyzed. According to the three different representations of topic labels, based on phrases, abstracts, and pictures, the topic labeling methods are classified into three types. Then, centered on improving the interpretability of topics, with different types of generated topic labels utilized, the relevant research in recent years is sorted out, analyzed, and summarized. The applicable scenarios and usability of different labels are also discussed. Meanwhile, methods are further categorized according to their different characteristics. The focus is placed on the quantitative and qualitative analysis of the abstract topic labels generated through lexical-based, submodular optimization, and graph-based methods. The differences between separate methods with respect to the learning types, technologies used, and data sources are then compared. Finally, the existing problems and trend of development of research on automatic topic labeling are discussed. Based on deep learning, integrating with sentiment analysis, and continuously expanding the applicable scenarios of topic labeling, will be the directions of future development.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Purpose/Significance] The introduction of national poverty alleviation policies and rural revitalization strategies has thrust the issue of education for left-behind children into the spotlight of scholarly attention. Education, far beyond serving as a mere instrument for personal growth and human capital accumulation for left-behind children, emerges as a pivotal measure in consolidating rural poverty alleviation endeavors and breaking the transmission of intergenerational poverty in China. It stands as a vital force propelling the future of rural revitalization. Yet, the existing literature on the education of left-behind children remains sporadic and dispersed. A more profound organizational effort, integrating, synthesizing, and evaluating this scattered literature, is imperative to establish a foundational framework for future research, fostering more cohesive and focused research endeavors. Presently, literature review studies primarily fall into three categories: qualitative review methods, meta-analysis, and bibliometric analysis methods employing tools like Citespace. This study sets out to achieve a systematic and comprehensive understanding of education-related issues for rural left-behind children through text mining methods grounded in topic models. [Method/Process] The advent of artificial intelligence and machine learning technologies has empowered the processing and analysis of vast amounts of textual data. Previous research, employing latent dirichlet allocation (LDA) topic models, successfully mined texts related to teacher team construction reform policies, internationalization in higher education literature, news reports, and online comments. In this study, a corpus was meticulously constructed using abstract texts extracted from 2037 journal articles published between 2002 and 2023. The structural topic model (STM) was chosen for topic modeling, overcoming the limitations associated with LDA, with a specific emphasis on exploring the diversity and dynamism of topics within the existing literature. [Results/Conclusions] The culmination of this research effort identified eight distinct research themes: psychological well-being, factors leading to left-behind children, macro-level coping strategies, types of guardianship, review studies, family education, media literacy, and micro-level coping strategies. By synergizing document metadata information, the study systematically unraveled the evolving trends of these topics over time, providing crucial insights into potential shifts in the focus of left-behind children's education research. It is essential to note that this study, while collecting abstracts instead of full texts, may not capture the entirety of information contained in complete research articles. Future research endeavors should explore left-behind children's education more comprehensively, leveraging full-text mining techniques for a more nuanced understanding of this critical subject.

[Objective] Because the decision-making process of the government follows the principle of “satisfaction” rather than “optimization”, attitude becomes a key element in the decision-making process. From this perspective, this study examined how the ecological governance attitude of higher-level government acts on the attention allocation of lower-level governments. [Methods] Based on the topic model and machine learning techniques, this study examined 3648 provincial and municipal government work reports (2010-2021) sentence by sentence. Then the ecological governance attitude and attention allocation of the provincial and municipal governments were measured and analyzed. [Results] The study found that the ecological governance attitude of higher-level governments can directly and indirectly act on the attention allocation of lower-level governments, and the ecological governance attitude of lower-level governments themselves plays a mediation role in the process. There are differences in the influence of different attitudes on attention. The increase of negative attitude of higher-level governments towards ecological governance significantly improves the attention allocation of lower-level governments, indicating the “deterrent effect” of negative attitude; while the improvement of the positive attitude of higher-level governments reduces the attention allocation of lower-level governments, which indicate the competitive relationship and “crowding out effect” of different issues on local governments’ attention. The results were also verified through robustness tests, including measuring government attention by governance data on the Internet, and testing the “attitude-attention” relationship under the issue of “production safety”. [Conclusion] This article argued that the government should pay attention to the role of government attitude as a management signal, and take the expression framework of government attitude as an important management tool, to reasonably regulate the attention allocation of ecological governance of local governments.

[Purpose/Significance] The introduction of national poverty alleviation policies and rural revitalization strategies has thrust the issue of education for left-behind children into the spotlight of scholarly attention. Education, far beyond serving as a mere instrument for personal growth and human capital accumulation for left-behind children, emerges as a pivotal measure in consolidating rural poverty alleviation endeavors and breaking the transmission of intergenerational poverty in China. It stands as a vital force propelling the future of rural revitalization. Yet, the existing literature on the education of left-behind children remains sporadic and dispersed. A more profound organizational effort, integrating, synthesizing, and evaluating this scattered literature, is imperative to establish a foundational framework for future research, fostering more cohesive and focused research endeavors. Presently, literature review studies primarily fall into three categories: qualitative review methods, meta-analysis, and bibliometric analysis methods employing tools like Citespace. This study sets out to achieve a systematic and comprehensive understanding of education-related issues for rural left-behind children through text mining methods grounded in topic models. [Method/Process] The advent of artificial intelligence and machine learning technologies has empowered the processing and analysis of vast amounts of textual data. Previous research, employing latent dirichlet allocation (LDA) topic models, successfully mined texts related to teacher team construction reform policies, internationalization in higher education literature, news reports, and online comments. In this study, a corpus was meticulously constructed using abstract texts extracted from 2037 journal articles published between 2002 and 2023. The structural topic model (STM) was chosen for topic modeling, overcoming the limitations associated with LDA, with a specific emphasis on exploring the diversity and dynamism of topics within the existing literature. [Results/Conclusions] The culmination of this research effort identified eight distinct research themes: psychological well-being, factors leading to left-behind children, macro-level coping strategies, types of guardianship, review studies, family education, media literacy, and micro-level coping strategies. By synergizing document metadata information, the study systematically unraveled the evolving trends of these topics over time, providing crucial insights into potential shifts in the focus of left-behind children's education research. It is essential to note that this study, while collecting abstracts instead of full texts, may not capture the entirety of information contained in complete research articles. Future research endeavors should explore left-behind children's education more comprehensively, leveraging full-text mining techniques for a more nuanced understanding of this critical subject. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South-Central Minzu University (Natural Science Edition) is the property of Journal of South-Central Minzu University (Natural Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional text keyword extraction methods ignore the contextual semantic information and cannot solve the problem of ambiguity of a word, so the extraction effect is not ideal. Based on the LDA and BERT models, this study proposes the LDA-BERT-LightGBM (LB-LightGBM) model. The LDA topic model is selected to obtain the topic of each review and its word distribution, candidate keywords are filtered out according to the threshold, and the filtered words and the original review text are spliced and input into the BERT model. The word vector training is performed to obtain the word vector containing the text topic, so the text keyword extraction problem is converted into a two-classification problem through the LightGBM algorithm. The textrank algorithm, LDA algorithm, LightGBM algorithm and the proposed LB-Light GBM model are compared through experiments on the accuracy rate P, recall rate R and F1 of text keyword extraction in the present study. The results show that when TopN takes 3～6, the average value of F1 is 3.5% higher than that of the optimal method, indicating that the extraction effect of this method is generally better than that of the comparison method selected in the experiment, and the text keywords can be found more accurately. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As regression testing size and cost increase,test suite reduction becomes more important to promote its efficiency.Du-ring the selection of test suite subset,we are supposed to consider the representativeness and diversity of subset,and apply an effective algorithm to solve it.Aimed at test suite reduction,a submodular function maximization based algorithm,SubTSR,is proposed in this paper.Although the introduced discrete optimization problem is an NP-hard problem,the heuristic greedy search is used in this paper to find the suboptimal solution with approximation guarantee by exploiting the submodularity of the objective function.To validate the effectiveness of the SubTSR algorithm,the SubTSR algorithm is experimented on fifteen datasets with changes of topic count in LDA and distance for similarity measurement,and compared with other test suite reduction algorithms about the average percentage of fault-detection,fault detection loss rate,first faulty test's index and other metrics.The experiment results show that the SubTSR algorithm has significant improvement in fault detection performance compared with other algorithms,and its performance remains relatively stable on different datasets.Under the text representation change due to topic count change,the SubTSR combined with Manhattan distance keeps relatively stable compared with other algorithms.

The traditional topic model algorithm does not make full use of the semantic relationship between words and the context, which leads to the inconsistency of topic semantics and poor interpretability.A hot topic recognition method based on the joint training of the LDA2Vec topic model (NS-LDA2Vec) was thus proposed. This method expanded the Skip-gram model to jointly train the initialized document vector and pivoted word vector to obtain the context vector, and then used the vector to predict the context word of the pivot word, thereby embedding topic information into the word representation and document in the representation; the sum of the negative sampling loss and the Dirichlet likelihood term in the prediction process was minimized, which resulted in a better interpretable text representation. The results show that the F1 value obtained by the proposed method can reach up to 0.898. Compared with the traditional LDA topic model, the topic relevance is improved by about 9% on the hot topic classification task, which can effectively improve the effect of the topic recognition task.

[Purpose/Significance] The smartness of public culture is one of the important ways to realize the innovation of public cultural service and management mode. The extraction and analysis of the description information of the smartness of public culture is helpful to understand the current situation of intelligent construction, and is of great significance for promoting the better development of intelligent construction of public cultural institutions. [Method/Process] On the basis of reviewing related research on the smartness of public culture, we obtained information on the official website pages of domestic provincial and sub-provincial city libraries and museums through crawlers, and used topic models and cluster analysis methods to conduct intelligent description for analysis. [Results/Conclusions] The thematic modeling results of libraries and museums reflect the similarities of the two types of institutions, such as user-centric, demand-oriented, smart resource construction, and high technical sensitivity. The library related studies pay more attention to "acquisition" and "circulation". The museum related studies pay more attention to "interaction" and "protection". Clustering and visual analysis reflect that libraries have made more achievements in self-service borrowing and returning facilities, smart equipment, etc. The smart construction of museums mainly focuses on digital interaction, smart protection, and smart management.

[Purpose/Significance] This paper aims to explore whether there are differences in the title and abstract, citation content, and full-text content on research topic, and analyze whether the topic content in the title and abstract can reveal the research content of the full text and the effect of the citation content on the content of the citing literature, so as to provide theoretical support for analyzing the research content of the full text based on the title and abstract of the literature. [Method/Process] This paper conducts an empirical study using Chinese journal papers in the field of COVID-19, extracts feature words from the titles and abstracts, citations and full-text contents of the literature, uses the clustering algorithm to cluster the feature words, and then uses manual interpretation to identify the research topics, and conducts a comparative study to analyze the topic differences among the three parts. [Results/Conclusions] The results show that: the research topics are different in the title and abstract, citation content and full-text content of the literature; compared with the title and abstract, the full text contains more topic content, but the difference in the topic content is small, so the topic content in the title and abstract can be used to represent the research content of the full text; the content of the citation is related to the topic of the citing literature, and they can complement each other.

[Purpose/Significance] Online encyclopedias such as Wikipedia include a large number of concepts. However, in such encyclopedias, there are no clear divisions between concepts and concepts, between concepts and disciplines, and between disciplines and disciplines. It embarrasses junior researchers regarding a certain discipline to obtain domain relevant knowledge in a systematic and low-efficiency manner. [Method/Process] To obtain information in a specific subject area and organize knowledge better, a new algorithm is designed for subject division. Specifically, approaches in complex network analysis are introduced for subject division, which helps to build a topic text network by the classical topic model. Then, the overlapping community label propagation algorithm is improved to identify the boundaries of different subject divisions. [Results/Conclusions] Finally, 300 Wikipedia entry texts were investigated as samples to evaluate the effectiveness of the proposed algorithm. Categories of experiments were conducted to analyze the community structure of the entry network and the complexity of the subject division, which helps to provide a corpus to build a subject knowledge base.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abnormal prescriptions refer to abnormal prescriptions prescribed by doctors for patients. Abnormalities in medical prescription, such as drug abuse and incorrect medicine, will affect the efficiency of treatment and even result in serious consequence. Because of some subjective or objective reasons, there exist more or less abnormal prescriptions. The detection of the abnormal prescriptions can improve the efficiency of the patient??s medical treat-ment and reduce the cost of the medical treatment. It is of great significance to drug abuse, polypharmacy and incorrect medicine. For this reason, this paper proposes an extended topic model based abnormal medical prescri-ption detection method, which can automatically detect the abnormal prescription from a large number of prescri-ption data. In addition, for each new prescription, this method can determine whether its diagnose and medicine match, and then determine whether it is normal or not. Compared with other abnormal detection algorithms, this method has a wider range of applications, not only can be used to detect abnormal prescriptions, but also can be used to detect the matching relations between other features. This method has been implemented and its validity has been verified in a real prescription dataset.

It is a hot research topic to obtain user interest by analyzing user's UCC content. Identifying user interest changes in social networks is of great significance for providing high-quality personalized network services. For the cold - start' users with poor UGC, LDA topic model is not effective to obtain their interest, and it is more difficult to identify the interest changes of them. To solve this problem, LDA is firstly used to obtain network top ices, and by which the topic-tag model is constructed. Furthermore, the spatial vector similarity calculation method based on tag association is used to construct the user-topic model. Combined with the learning model, a user interest change recognition algorithm is proposed. The proposed algorithm is applied to the large-scale data set collected by the microblog website, and the network topic, the core users of the network topic and their probability distribution on the interest topic are obtained more accurately, thus the identification problem of user interest change in the model is solved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The traditional topic model algorithm does not make full use of the semantic relationship between words and the context, which leads to the inconsistency of topic semantics and poor interpretability. A hot topic recognition method based on the joint training of the LDA2Vec topic model (NS-LDA2Vec) was thus proposed. This method expanded the Skip-gram model to jointly train the initialized document vector and pivoted word vector to obtain the context vector, and then used the vector to predict the context word of the pivot word, thereby embedding topic information into the word representation and document in the representation; the sum of the negative sampling loss and the Dirichlet likelihood term in the prediction process was minimized, which resulted in a better interpretable text representation. The results show that the F1 value obtained by the proposed method can reach up to 0.898. Compared with the traditional LDA topic model, the topic relevance is improved by about 9% on the hot topic classification task, which can effectively improve the effect of the topic recognition task. [ABSTRACT FROM AUTHOR]

Sentiment community detection in social networks would be very valuable in many areas, such as public health, public opinion monitoring and so on. A framework of sentiment community detection was established on Sina Weibo. Firstly, the sentiment expression features and lexicon of Weibo were combined, and the classification model SL-SE-NB ( naive Bayes algorithm based semi-lexicon and semi-emoji) was proposed to predict the sentiment polarity of texts. And then, the UTK ( user-topic-keywords) model based on LDA ( latent Dirichlet allocation) was proposed to extract user topics. Based on LP A ( label propagation algorithm) and adding topic concepts, SMB-LPA ( label propagation algorithm based seeds and min-edge betweenness) was proposed to discovery sentiment community. Final! y, the experimental results proved the effectiveness and efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To solve the problem of lacking user ratings and inaccuracy of user topics model for danmaku video website, this paper proposed a danmaku video recommendation algorithm combing topic model and collaborative filtering ( DRCIT). Firstly, it constructed an implicit rating matrix with user behavior, and obtained the similarity of project rating. Secondly, it established an LDA topic model to generate project topics and calculate the similarity of project topics. Thirdly, it generated a comprehensive project similarity, and calculated the user-project prediction score. Then, through sigmoid penalty, it established the new similarity matrix of project topics and got user-project topic similarity combined with user history behavior. Finally, it took user-project topic similarity as weight to multiply with prediction score to get the final prediction score. The comparison experiments show that the algorithm can get reasonable recommendation results and improve the accuracy of recommendation. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The mining and analysis of user - generated content is an effective way to obtain microblog topic. Aiming at the problem of cold start due to the lack of user tags, this author proposes a microblog topic generation method by integrating user tags and topic tags. Firsdy, the user tag was used as the feature item to compress the sparse vector and calculate the similarity of user tag. Secondly, LDA topic model was used to extract the topic of users microblog by which the topic tag was generated. Thirdly, the two kinds of tags were integrated to establish the similarity model of user tag topic and obtain the topic of microblog. The proposed method was applied to the microb¬log data set and the microblog topic represented by hot tags was obtained. While it is basically consistent with the results gotten with LDA topic model, it is more representative [ABSTRACT FROM AUTHOR]

Chest X ray is the preferred choice for patients' chest examinations and plays an important role in the diagnosis and treatment of patients. Doctors write chest X-ray diagnostic reports based on their own experience and habits. For some subjective or objective reasons, they will issue some abnormal diagnostic reports that do not match the diagnostic conclusions. Therefore, it is of great significance to carry out abnormal detection of the diagnostic reports. Chest X-ray diagnostic reports have many unknown words and sparse high-dimensional data and lack of a lot of effective labeling. Traditional methods are ineffective in detecting abnormal chest X-ray diagnostic reports. Therefore, this paper proposes an abnormal chest X-ray diagnostic report detection method based on topic model. Firstly, the bidirectional LSTM-CRF model is used to combine the character-level features in the chest radiograph diagnosis reports to obtain the specific medical terminology features, so as to solve the problem that the diagnosis reports have many unknown words and are described freely. Secondly, based on domain knowledge and template, the chest X-ray diagnosis reports are extended effectively to alleviate the problem of data spar-sity. Finally, the LDA model is used to determine whether the image description in the diagnosis reports match the characteristics of the diagnosis conclusion, so as to detect the abnormal chest X-ray diagnosis reports. Experiments show that the accuracy of abnormal detection is 92.82 and the recall rate is 69.54 when the threshold is 2. The proposal has higher abnormal detection performance than the traditional methods. [ABSTRACT FROM AUTHOR]

The paper aims at the problem that traditional LDA-based topic models on short texts are susceptible to sparseness, noise, and redundancy. Firstly, the changes of text feature representation and the development of topic models on short texts are reviewed. The generation process of the Latent Dirichlet Allocation (LDA) model and the Dirichlet Multinomial Mixture (DMM) model and the corresponding Gibbs sampling parameter derivation are systematically summarized. Regarding the optimal number of topics in the topic model, a detailed comparison of the four common optimization indicators is given. Finally, the extended research of the topic model in the past two years and its simple application in network public opinion are analyzed, and the research direction and focus of the future topic model are pointed out. [ABSTRACT FROM AUTHOR]

A topic model is an analysis method that extracts latent topics from words of documents. Topic models are used in many fields nowadays, and they are used for marketing or medical data, where data of a subject are often repeatedly measured. Although topic models for longitudinal data have been already proposed, a topic model that can be applied to more diverse repeated measures data needs to be proposed. Then, we proposed a topic model for repeated measures data and evaluated its performance using real-world prescription drug data of a hospital. As a result, the proposed method showed superior performance to existing methods. [ABSTRACT FROM AUTHOR]

LDA is based on the bag-of-words, which simplifies the complexity of modeling, but makes the semantic coherence of topics poor, and text representation ability is not strong. To solve this problem, this paper proposed the semantic distribution similarity based topic model. This model used GPU (generalized Pólya urn) model to add word-word and document-topic semantic distribution similarity to guide topic modeling under the framework of EM (expectation maximization) algorithm, which weakened the effect of bag-of-words hypothesis on topics from the semantic association level. Experiments on four public datasets show that the semantic distribution similarity based topic model is superior to the currently popular topic modeling algorithms in terms of topic semantic coherence and text classification accuracy, and the model improves the convergence speed and topic accuracy. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the recommendation list :is biased towards popular projects and with poor diversity, this paper proposed the ARIFDP ( aggregation recommendation algorithm for embedding item fatigue and diversity preference) algorithm. First, it analyzed the user's diversity preferences by using the historical feedback data of the user to derive the user, got the degree of diversification. And then it constructed a project fatigue function negatively related to the number of evaluations; eventually aggregated the matrix decomposition and the project fatigue function, and the inclusion of various propensity degrees adjusted the weight of the fatigue function of the project, and increased the probability of the proposed project. Experimental results show that the ARIFDP algorithm can effectively improve the diversity of recommendation results on the premise of ensuring accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the increasingly rich multi language information resources and multi-label data in news reports and scientific literatures, in order to mining the relevance between languages and the correlation between data, this paper proposed labeled bilingual topic model, which was applied on cross-lingual text classification and label recommendation. First of all, it could assume that the keywords in the scientific literature are relevant to the abstract in same article. And then it extracted the keywords and regarded it as labels, and aligned the labels with topics in topic model, instantiated the " latent" topic. Secondly, this paper trained the abstracts in article through the proposed topic model. Finally, it classified the new documents by cross-lingual text classifier, and also recommended the labels. The experiment result show that micro-F1 measure reaches 94. 81 % in cross-lingual text classification task, and the recommended labels also reflects the sematic relevance with documents. [ABSTRACT FROM AUTHOR]

This paper proposed a collaborative topic regression model based on the preference for frequent topic sets to address the item-cold-start problem in academic paper recommendation. The algorithm took into account the user' s preference for research hotspots when selected academic papers, and used frequent topic sets to represent research hotspots. So, it expressed user' s preference for research hotspots as the user's preference for frequent topic sets. Firstly, it obtained the papers-topic probability distribution matrix through LDA algorithm and filter out the topics with higher probability. Then, the algorithm mined the frequently-occurring topic sets and gets the relationships between papers and frequent topic sets. Finally, it used the user's preference for frequent topic sets for the prediction of unknown scores. Experiments on CiteULike datasets show that the algorithm improves the recall, accuracy and RMSE over the matrix factorization model and the collaborative topic regression model. [ABSTRACT FROM AUTHOR]

Microblogs have a large amount of data but a few characters in the text, and their features are sparse. In order to improve the retrieval precision, we propose a microblog retrieval model combining BTM and graph theory. The lexical semantic correlation is used to calculate the correlation between features with labels in microblog text. Then we construct a bi-term topic model，use JSD distance to calculate the correlation of pair words in the short text that mapped to the model. Thirdly，we extract the entity and graph structure in CN-DBpedia，and then use the SimRank algorithm to calculate inter-entity correlation between graph structures. The above three correlations are the final correlation of the model. Finally，the Sina Weibo data set is used for the retrieval experiments. Experimental results show that compared with the retrieval model based on the combination of the implicit Dirichlet distribution algorithm and graph theory and the system model based on open data correlation and graph theory, the performance of the new model is significantly improved in MAP, accuracy and recall rate, indicating that the model has better retrieval performance. [ABSTRACT FROM AUTHOR]

In order to obtain the topic distribution of bilingual documents effectively, we propose a phrase-based Khmer-Chinese bilingual LDA topic model* We modify the bag-of-word model in the traditional LDA topic model and incorporate the concept of phrase (N-gram). The method considers the word order and context of the article in the topic prediction process and applies it to the bilingual environment of comparable corpus. It is based on a three-layer Bayesian network model. Under this framework，we firstly collect comparable Chinese and Khmer corpus，and each pair of bilingual comparable corpus shares a common topic distribution. And then we introduce the topic model of discovery topic and topic phrase： the topic of each word is firstly sampled； then its status is sampled as a phrase； and finally words from a particular topic phrase distribution are sampled. Experimental results show that the phrase-based bilingual LDA topic model is more capable of grasping the topic of the article than general bilingual LDA models and has better topic prediction ability. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Purpose/significance] This paper comprehensively summarizes the application of topic models in public policy texts, which helps researchers learn from existing research results and provides theoretical and practical support for future development. [Method/process] This paper used bibliometric analysis to study from the perspectives of time trend, organization distribution, periodical distribution, etc., and summarized the application status in detail. Secondly, the LDA topic model was used to identify the main international and domestic research directions and conducted a comparative analysis. Finally, this paper summarized the problems in the application and proposed future prospects. [Result/conclusion] The application of topic models in the analysis of public policy texts is on the rise overall and has broad prospects. The starting time of domestic and foreign research is equivalent, but domestic research needs to be improved in terms of research scope, research depth, cooperation methods, and research methods. In addition, in the future development, there are problems with the applicability of the topic model's own methods and the granularity of research content. It is necessary to further combine the characteristics of public policy texts to improve the topic model and refine research efforts.

Micro-blog data has the characteristic of real-time,volume,short-text,and noise-rich.So it is a challenge for the traditional topic detection technology.A novel micro-blog topic detection algorithm based on topic model was proposed.Firstly,the micro-blog data was expressed as text word matrix and word relation matrix.The topic word was extracted from the two vectors.Secondly,the topic model was obtained with clustering.Finally,the topic detection of micro-blog was obtained by clustering text and topic model.Experimental results show that the algorithm proposed can effectively detection the text topic,and with the best parameter group of precision,recall rate,F,and the value F is about 95%.

In recent years, the explosive growth of the number of App brings difficulties for user to find a suitable App. There are many limitations of the traditional recommendation approaches when they are applied to App, such as the cold start problem and different choice bias on different types of applications. This paper proposed a personalized recommendation algorithm TGRW, which was based on topic grouping and random walk. Because users have different biases and underling choice factors on the App of different categories, TGRW firstly divided App into categories based on their description information. Then it constructed a triple tuple graphic model consisting of user, App group and App. By applying random walk to calculate the preferential probability of user to each App, it obtained the recommendation list. Extensive experiments on the real data set shows TGRW gains significant improvements on recommendation performances than other methods. [ABSTRACT FROM AUTHOR]

Since the traditional methods of micro-blog sentiment analysis ignore the influence of images, the special symbol information and the context semantic information, which leads to the accuracy rate of the emotion analysis method is not high. Therefore , this paper presented a micro-blog sentiment method, which was illustrated and based on transferred variable. Firstly, it paper constructed an unsupervised sentiment analysis model USAMTV, which was based on transferred variable, to analysis the distribution of emotion in the text. That was to say, the emotion and topic were categorized by introducing the sentimental transferred variable of conjunction and thematic transferred variable of reposting symbols. Then it introduced the image factors as emotional weight to decide the emotional distribution of the text. Finally, it obtained the overall sentimental orientation of the micro-blog. Compared with the JST model and the ASUM model, this proposed method had higher accuracy in the real-world test dataset. The experimental results show' that the proposed method can predict the emotional tendency of microblog more accurately. [ABSTRACT FROM AUTHOR]

In order to solve the problems of data sparseness and only using the user's rating behavior to recommend in the traditional collaborative filtering algorithm, a kind of matrix factorization recommendation algorithm combining users' reviews was proposed. First, the algorithms utilized topic model to generate review topics distribution, and extracted accurate user interests and items characteristics by integrating the score. Then, combined with users' interests and item characteristics, the nearest neighbors of users and items were calculated according to the cosine similarity. Last, the nearest regularization terms were introduced into the matrix decomposition model.The USRMF was compared with the traditional collaborative filtering algorithm and the regularization matrix decomposition algo-rithm.The experimental result shows that the USRMF can improve the accuracy of recommendation in sparse datascts. [ABSTRACT FROM AUTHOR]

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The state-of-the-art studies started paying attention to comprehensively analyze geographical information,comment information and social information, but there is still insufficient information mining. To this end, this paper proposed a joint probabilistic generative model of multi-information fusion. Firstly, the framework learned interest topics of users and POIs through textual information,by exploiting the aggregated hierarchical Dirichlet process model,which could automatically learn the number of topics,to replace latent Dirichlet allocation model. Secondly ,according to the kernel density estimation method , whose bandwidth depended on the check-in distribution , the framework conducted personalized modeling of geographic information. Thirdly,it also took consideration of sequential patterns,which was the impact of the visited location to the non-visited location. Then , it modeled social relevance comprehensively. At last,based on the joint probabilistic generative model,this paper proposed the TGSS-PGM model, exploiting multi-type contextual information and incorporating these factors effectively. Experimental results in real world social net-work show that the proposed model outperforms state-of-the-art recommendation algorithms in terms of precision and rating error. [ABSTRACT FROM AUTHOR]

In the interest of solving the review honesty problems in current recommendation algorithms based on review analysis, and the data sparsity problems in traditional collaborative filtering algorithm, this paper involved the modified user preferences and user trust into traditional collaborative filtering algorithm, and proposed a new user-based collaborative filtering algorithm based on user ratings, reviews and user trust by analyzing the topic distributions and feedback information included in reviews from users. In detail, with the consideration of users’ reviews, it investigated the distribution of item features on different topics, and the level of preference of different users, to generate topic distribution of users’ reviews. To continue, this paper calculated the value of review diversity to magnify distinctive features in topic distribution, and generated the value of review helpful-degree by utilizing review feedback data to rectify user preferences and reduce the influences from spam reviews. On this basis, it further introduced user trust to enhance the accuracy of user similarity. The results of experiment on real dataset show that the proposed algorithm can improve the quality of prediction performance. [ABSTRACT FROM AUTHOR]

Latent Dirichlet allocation(LDA) topic model is an effective tool to process unstructured documents. But it is built on bag-of-words(BOW) model assumption, which regard each document as a combination of the word, neither the order relationship between documentsnor the order relationship between words is concerned. To improve current model's accuracy, this paper came up with the centroid-word based context topic model, this model was based on the theory that the topic of a word in a document had strong relationship of the word which near by. When calculating the topic distribution for each word, it regared the word as the center, extend before and after several words as the window, and then performed a calculation on each window. This approach would generate the corresponding order of each window, the same as the order of words, and because of the contexts of each word were different, so the distribution of each word had relationship with the location the word in the corresponding document. Experiments show that the centroid-word based context topic model has the better accuracy and convergence rate on unknown datasets. [ABSTRACT FROM AUTHOR]

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To solve the problem of content sparsity and lack of context information existed inherently in short texts, this paper proposed a biterm topic model (BTM) incorporating word vector features LF-BTM based on BTM. This model introuded latent feature model which utilized its abunnant word vector information to offset the data sparsity. Generation of words in each biterm was influenced jointly by topic-word multinomial distribution and latent features model in the improved generative process. Parameters in the model could be learned by of Gibbs sampling method. Experimental results on real-world short texts datasets demonstrate that the model can integrate word vectors trained from external general large-scale corpora to produce significant improvements on topic coherence. [ABSTRACT FROM AUTHOR]

In modern society, the micro-blog is an important social network. Repost is one of the most important features of micro-blog. However, the previous studies should have paid close attention to the motivation of repost and the interest-repost. Aiming at the feature of repost, the paper proposed an AT-IR model which was based on the original AT model tapping the interest-reposting to find the interest of micro-blog users. Besides , by means of crawling the micro-blog experimental data sets , this model is better than the AT model in the aspect of extraction accuracy. In addition , this method has shown the expansibility in the aspect of other subject model. [ABSTRACT FROM AUTHOR]