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

Schwarz, Carlo

doi:10.1177/1536867x1801800107

Cited by 56 publications

(54 citation statements)

References 10 publications

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“…As explained in Schwarz (2018), Latent Dirichlet Allocation (LDA) is composed of two parts. The first is a probabilistic model describing the text data as a likelihood function.…”

Section: Methodsmentioning

confidence: 99%

“…The authors employ Latent Dirichlet Allocation (LDA), a machine learning algorithm developed by Blei et al (2003) which allows for the automatic clustering of 1,892 ECB Board speeches. The aim of the current paper is to introduce the LDA methodology as presented in Schwarz (2018) and obtain results using the Idagibbs Stata command. We analyze 45,346 entries or passages of the Federal Open Market Committee (FOMC) during the period 2003-2012, with the goal of identifying the evolution of the different topics discussed by the members of the FOMC.…”

Section: Introductionmentioning

confidence: 99%

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Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

Edison

Cárcel

2020

Applied Economics Letters

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This paper applies Latent Dirichlet Allocation (LDA), a machine learning algorithm, to analyze the transcripts of the U.S. Federal Open Market Committee (FOMC) covering the period 2003-2012, including 45,346 passages. The goal is to detect the evolution of the different topics discussed by the members of the FOMC. The results of this exercise show that discussions on economic modelling were dominant during the Global Financial Crisis (GFC), with an increase in discussion of the banking system in the years following the GFC. Discussions on communication gained relevance toward the end of the sample as the Federal Reserve adopted a more transparent approach. The paper suggests that LDA analysis could be further exploited by researchers at central banks and institutions to identify topic priorities in relevant documents such as FOMC transcripts.

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“…As explained in Schwarz (2018), Latent Dirichlet Allocation (LDA) is composed of two parts. The first is a probabilistic model describing the text data as a likelihood function.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

Edison

Cárcel

2020

Applied Economics Letters

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“…Its performance will be tested by processing real-time information of the Twitter platform via the Twitter API, and more advanced text preprocessing techniques will be incorporated to improve the quality of classification of the sentiment. Further enhancements include the portability to other platforms such as mobile devices and the incorporation of Latent Dirichlet Allocation (LDA) technique to automatically identify the number of attributes of corpus [57][58][59][60].…”

Section: Discussionmentioning

confidence: 99%

Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

Wang

Youn

2018

Applied Sciences

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The rapid growth in social networking services has led to the generation of a massivevolume of opinionated information in the form of electronic text. As a result, the research on textsentiment analysis has drawn a great deal of interest. In this paper a novel feature weighting approachis proposed for the sentiment analysis of Twitter data. It properly measures the relative significanceof each feature regarding both intra-category and intra-category distribution. A new statistical modelcalled Category Discriminative Strength is introduced to characterize the discriminability of thefeatures among various categories, and a modified Chi-square (2)-based measure is employed tomeasure the intra-category dependency of the features. Moreover, a fine-grained feature clusteringstrategy is proposed to maximize the accuracy of the analysis. Extensive experiments demonstrate thatthe proposed approach significantly outperforms four state-of-the-art sentiment analysis techniquesin terms of accuracy, precision, recall, and F1 measure with various sizes and patterns of training andtest datasets.

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“…Depolanan verinin çok büyük oranda metin verisi olduğu göz önüne alındığında, metin verilerinin otomatik analizi oldukça önemli bir araştırma problemi haline dönüşmektedir. Bu doğrultuda, [1][2][3][4]. Dokümanlar gibi ayrık verileri modellemek için geliştirilen üretici grafiksel bir model olan GDA dokümanı oluşturan gizli konuları ortaya çıkarmaktadır [5].…”

Section: Introductionunclassified

Tıp Veri Kümesi için Gizli Dirichlet Ayrımı

Ekinci¹,

Omurca²,

Kirik³

et al. 2020

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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Öz Bilimsel çalışmalarda ilgili alandaki literatürün incelenmesi oldukça önemli bir aşamadır. Literatür insan tarafından tarandığında, geniş kapsamlı bir inceleme yapılması mümkün olamamakta, ya da böyle bir arama çok uzun zaman almaktadır. Öte yandan literatürün otomatik olarak taranması derinlemesine bir anlamsal analizi mümkün kılmamaktadır. Bu çalışma kapsamında Türkiye'deki araştırmacılar tarafından yayınlanmış tıp makalelerinin otomatik ve anlamsal analizini gerçekleştiren bir konu modelleme yöntemi olan Gizli Dirichlet Ayrımı (GDA) uygulanmıştır. Deneysel çalışma, yıllara göre bir tıp veritabanı olan PubMed'den elde edilen son 11 (on bir) yıldaki yayınla tıp literatüründeki makaleler üzerinde gerçeklenmiştir. Deneysel sonuçlar incelendiğinde, son 11 (on bir) yılda trend olan çalışma başlıklarının başarılı bir şekilde keşfedildiği gözlenmiştir.

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

Cited by 56 publications

References 10 publications

Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

Text data analysis using Latent Dirichlet Allocation: an application to FOMC transcripts

Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

Tıp Veri Kümesi için Gizli Dirichlet Ayrımı

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