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AnRPackage for Fitting Topic Models","{\"openalex\":\"W1753361524\",\"doi\":\"10.18637/jss.v040.i13\"}","openalex","2026-05-14T12:35:16.259856783","2026-05-21T16:09:49.325996613","doi","10.18637/jss.v040.i13","arxivId","openalexId","W1753361524","title","abstractText","Topic models allow the probabilistic modeling of term frequency occurrences in documents. The fitted model can be used to estimate the similarity between documents as well as between a set of specified keywords using an additional layer of latent variables which are referred to as topics. The R package topicmodels provides basic infrastructure for fitting topic models based on data structures from the text mining package tm. The package includes interfaces to two algorithms for fitting topic models: the variational expectation-maximization algorithm provided by David M. Blei and co-authors and an algorithm using Gibbs sampling by Xuan-Hieu Phan and co-authors.","publishedDate","2011-01-01","publishedYear",2011,"venue","Journal of Statistical Software","citationCount",1072,"authors","keywords",[141,142,30],"pdfUrl","topVenue",true,"Topic Modeling","Advanced Text Analysis Techniques","paper_W2601780063","Text as Data","{\"openalex\":\"W2601780063\",\"doi\":\"10.1257/jel.20181020\"}","2026-05-14T12:08:16.186375465","2026-05-21T15:44:01.192625625","10.1257/jel.20181020","W2601780063","An ever-increasing share of human interaction, communication, and culture is recorded as digital text. We provide an introduction to the use of text as an input to economic research. We discuss the features that make text different from other forms of data, offer a practical overview of relevant statistical methods, and survey a variety of applications. (JEL C38, C55, L82, Z13)","2019-09-01",2019,"Journal of Economic Literature",1090,[156,142,30],"Media Influence and Politics","paper_W2223092947","stm: An R Package for Structural Topic Models","{\"openalex\":\"W2223092947\",\"doi\":\"10.18637/jss.v091.i02\"}","2026-05-14T07:09:07.363173333","2026-05-21T09:56:21.193664178","10.18637/jss.v091.i02","W2223092947","This paper demonstrates how to use the R package stm for structural topic modeling. The structural topic model allows researchers to flexibly estimate a topic model that includes document-level metadata. Estimation is accomplished through a fast variational approximation. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest.","2019-01-01",1468,[30,169,141],"https://www.jstatsoft.org/index.php/jss/article/view/v091i02/v91i02.pdf","Data Analysis with R","paper_W3169483174","mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer","{\"openalex\":\"W3169483174\",\"doi\":\"10.18653/v1/2021.naacl-main.41\"}","2026-05-14T06:49:48.197867238","2026-05-21T09:22:48.212098882","10.18653/v1/2021.naacl-main.41","W3169483174","Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.","2021-01-01",2021,1543,[141,183,30],"https://aclanthology.org/2021.naacl-main.41.pdf","Natural Language Processing Techniques","paper_W2970419734","Text Summarization with Pretrained Encoders","{\"openalex\":\"W2970419734\",\"doi\":\"10.18653/v1/d19-1387\"}","2026-05-14T06:21:00.560433186","2026-05-21T08:45:25.205963209","10.18653/v1/d19-1387","W2970419734","Yang Liu, Mirella Lapata. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.",1605,[141,183,30],"https://www.aclweb.org/anthology/D19-1387.pdf","paper_W2137111266","dendextend: an R package for visualizing, adjusting and comparing trees of hierarchical clustering","{\"openalex\":\"W2137111266\",\"doi\":\"10.1093/bioinformatics/btv428\"}","2026-05-14T04:54:31.505221441","2026-05-21T07:13:55.186697286","10.1093/bioinformatics/btv428","W2137111266","UNLABELLED: dendextend is an R package for creating and comparing visually appealing tree diagrams. dendextend provides utility functions for manipulating dendrogram objects (their color, shape and content) as well as several advanced methods for comparing trees to one another (both statistically and visually). As such, dendextend offers a flexible framework for enhancing R's rich ecosystem of packages for performing hierarchical clustering of items. AVAILABILITY AND IMPLEMENTATION: The dendextend R package (including detailed introductory vignettes) is available under the GPL-2 Open Source license and is freely available to download from CRAN at: (http://cran.r-project.org/package=dendextend) CONTACT: Tal.Galili@math.tau.ac.il.","2015-07-23",2015,"Bioinformatics",1895,[169,209,30],"https://academic.oup.com/bioinformatics/article-pdf/31/22/3718/49036144/bioinformatics_31_22_3718.pdf","Data Visualization and Analytics","paper_W2112031167","Big Data, new epistemologies and paradigm shifts","{\"openalex\":\"W2112031167\",\"doi\":\"10.1177/2053951714528481\"}","2026-05-14T04:00:19.233731408","2026-05-21T05:57:29.202333202","10.1177/2053951714528481","W2112031167","This article examines how the availability of Big Data, coupled with new data analytics, challenges established epistemologies across the sciences, social sciences and humanities, and assesses the extent to which they are engendering paradigm shifts across multiple disciplines. In particular, it critically explores new forms of empiricism that declare ‘the end of theory’, the creation of data-driven rather than knowledge-driven science, and the development of digital humanities and computational social sciences that propose radically different ways to make sense of culture, history, economy and society. It is argued that: (1) Big Data and new data analytics are disruptive innovations which are reconfiguring in many instances how research is conducted; and (2) there is an urgent need for wider critical reflection within the academy on the epistemological implications of the unfolding data revolution, a task that has barely begun to be tackled despite the rapid changes in research practices presently taking place. After critically reviewing emerging epistemological positions, it is contended that a potentially fruitful approach would be the development of a situated, reflexive and contextually nuanced epistemology.","2014-04-01",2014,"Big Data & Society",2258,[209,30,224],"https://journals.sagepub.com/doi/pdf/10.1177/2053951714528481","Big Data Technologies and Applications","paper_W2016364797","Basic Content Analysis","{\"openalex\":\"W2016364797\",\"doi\":\"10.4135/9781412983488\"}","2026-05-14T01:56:30.41383659","2026-05-21T03:37:31.183734306","10.4135/9781412983488","W2016364797","Introduction Content Classification and Interpretation Techniques of Content Analysis Issues in Content Analysis","1990-01-01",1990,4045,[237,30],"Educational and Technological Research","paper_W2061504941","Answering the Call for a Standard Reliability Measure for Coding Data","{\"openalex\":\"W2061504941\",\"doi\":\"10.1080/19312450709336664\"}","2026-05-14T01:55:57.343664387","2026-05-21T03:35:31.184293654","10.1080/19312450709336664","W2061504941","In content analysis and similar methods, data are typically generated by trained human observers who record or transcribe textual, pictorial, or audible matter in terms suitable for analysis. Conclusions from such data can be trusted only after demonstrating their reliability. Unfortunately, the content analysis literature is full of proposals for so-called reliability coefficients, leaving investigators easily confused, not knowing which to choose. After describing the criteria for a good measure of reliability, we propose Krippendorff's alpha as the standard reliability measure. It is general in that it can be used regardless of the number of observers, levels of measurement, sample sizes, and presence or absence of missing data. To facilitate the adoption of this recommendation, we describe a freely available macro written for SPSS and SAS to calculate Krippendorff's alpha and illustrate its use with a simple example.","2007-04-01",2007,"Communication Methods and Measures",4113,[251,30,169],"Reliability and Agreement in Measurement","paper_W2963341956","","{\"openalex\":\"W2963341956\",\"doi\":\"10.18653/v1/n19-1423\"}","2026-05-14T00:29:27.266294578","2026-05-21T02:04:18.274126536","10.18653/v1/n19-1423","W2963341956","Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.",32075,[141,183,30],{"_58":263,"_60":264},[],[265],{"_63":266,"_65":267,"_67":268,"_69":269},1780520868763,{"_72":271,"_74":75,"_76":266,"_77":-5,"_78":26,"_79":26,"_80":26,"_81":-5,"_82":-5,"_83":84,"_35":85,"_86":87},[71,270],"[\"portal\",\"industries\"]","industries",[272,273,274,275,276,277,278,279,280,281,282,283],{"_15":343,"_29":344,"_31":-5,"_32":345,"_33":346,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":347,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":344,"_57":346},{"_15":339,"_29":340,"_31":-5,"_32":-5,"_33":341,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":342,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":340,"_57":341},{"_15":333,"_29":334,"_31":-5,"_32":335,"_33":336,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":337,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":334,"_57":336},{"_15":329,"_29":330,"_31":-5,"_32":-5,"_33":331,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":332,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":330,"_57":331},{"_15":325,"_29":326,"_31":-5,"_32":-5,"_33":327,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":328,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":326,"_57":327},{"_15":319,"_29":320,"_31":-5,"_32":321,"_33":322,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":323,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":320,"_57":322},{"_15":315,"_29":316,"_31":-5,"_32":-5,"_33":317,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":318,"_296":26,"_297":140,"_298":26,"_101":-5,"_56":316,"_57":317},{"_15":311,"_29":312,"_31":-5,"_32":-5,"_33":313,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":314,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":312,"_57":313},{"_15":307,"_29":308,"_31":-5,"_32":-5,"_33":309,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":310,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":308,"_57":309},{"_15":303,"_29":304,"_31":-5,"_32":-5,"_33":305,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":306,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":304,"_57":305},{"_15":299,"_29":300,"_31":-5,"_32":-5,"_33":301,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":302,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":300,"_57":301},{"_15":284,"_29":285,"_31":-5,"_32":-5,"_33":286,"_34":-5,"_35":36,"_37":-5,"_39":-5,"_40":287,"_42":-5,"_44":-5,"_288":289,"_290":-5,"_291":75,"_292":293,"_294":295,"_296":26,"_297":84,"_298":26,"_101":-5,"_56":285,"_57":286},"tax_ind_smartphone","智能手机","Smartphone","manual","taxonomyType","INDUSTRY","code","level","parentId","tax_ind_root","path","科技行业/智能手机","sortOrder","isLeaf","entityCount","tax_ind_robot","机器人","Robotics","科技行业/机器人","tax_ind_wearable","智能穿戴","Smart Wearables","科技行业/智能穿戴","tax_ind_energy","新能源与智能制造","New Energy & Smart Manufacturing","科技行业/新能源与智能制造","tax_ind_biotech","生物科技","Biotech","科技行业/生物科技","tax_ind_quantum","量子计算","Quantum Computing","科技行业/量子计算","tax_ind_blockchain","区块链",[324],"Blockchain","科技行业/区块链","Web3","tax_ind_enterprise","企业软件","Enterprise Software","科技行业/企业软件","tax_ind_security","网络安全","Cybersecurity","科技行业/网络安全","tax_ind_semi","半导体",[338],"Semiconductor","科技行业/半导体","芯片","tax_ind_cloud","云计算","Cloud Computing","科技行业/云计算","tax_ind_ai","人工智能",[348,349],"Artificial Intelligence","科技行业/人工智能","AI","AI 技术","env",{"_352":24},"SITE_BASE_URL"]

Computational and Text Analysis Methods 技术

Computational and Text Analysis Methods · 相关事件