LDAvis: A method for visualizing and interpreting topics

Sievert, Carson; Shirley, Kenneth E.

doi:10.3115/v1/w14-3110

Cited by 949 publications

(721 citation statements)

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“…A common practice to interpret the content of a topic is to rank 3 to 30 of its terms by their probability to belong to the topic (Sievert & Shirley, 2014). According to Sievert & Shirley (2014), evidence suggests that this is not an optimal approach to interpret results of an LDA model.…”

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

“…According to Sievert & Shirley (2014), evidence suggests that this is not an optimal approach to interpret results of an LDA model. The problem with this strategy is that the terms which are most common (i.e., frequent) will often appear at the top of the ranks of different topics.…”

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

“…This metric indicates how informative a given term is in determining the generation of a new topic in comparison with a randomly selected term. Sievert & Shirley (2014) combined the approaches of Bischof & Airoldi (2012) and Chuang et al (2012) and proposed LDAvis, a metric of relevance and a visualization tool as a method for interpreting topics.…”

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

“…The problem with this strategy is that the terms which are most common (i.e., frequent) will often appear at the top of the ranks of different topics. Some solutions have been proposed in recent years to handle this issue (Sievert & Shirley, 2014). Bischof & Airoldi (2012) suggested the Hierarchical Poisson Convolution that examines a given topic through a measure of terms frequency and exclusivity in the topic.…”

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

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The Personality Lexicon in Brazilian Portuguese: Studies with Natural Language

Peres¹

2018

Preprint

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iv Procura da poesia (...) Chega mais perto e contempla as palavras.Cada uma tem mil faces secretas sob a face neutra e te pergunta, sem interesse pela resposta, pobre ou terrível, que lhe deres:Trouxeste a chave? (...) Carlos Drummond de Andrade in A Rosa do Povo (1945) From March 1979Tired of all who come with words, words but no language I went to the snow-covered island.The wild does not have words.The unwritten pages spread out on all sides! I come upon the tracks of roe deer in the snow.Language but no words. Tomas Tranströmer in The Wild Market Square (1983, trad. Robin Fulton) v DedicatoryThis dissertation is dedicated to Professor Jacob Laros and to Professor Luiz Pasquali, who advised me during my postgraduate studies with inspiring wisdom, not only embracing my projects, but also kindly respecting my ideas and decisions, and patiently guiding me through my doubts and after my stumbles. It is an honor to be an apprentice and a friend of such masters.vi Agradecimentos (Acknowledgements)Um doutoramento não é resultado apenas da dedicação individual do estudante. Nesse sentido, faço os seguintes agradecimentos. À Renata e ao Bartholomeu, pelo amor e companheirismo. Aos meus pais, que sempre se esforçaram para oferecer aos filhos e a outros familiares oportunidades para o desenvolvimento educacional e profissional. Aos meus irmãos, cunhados e ao restante da minha grande e querida família mineira. À Thaís, Dudu e Raphael, a quem espero inspirar em seguir a carreira acadêmica. Aos meus professores da Educação Básica, que me ensinaram a aprender e inspiraram minha predileção pela ciência, artes e filosofia. Gostaria de lembrar especialmente das professoras Laudiene, Eliana, Magda Table 3. The Six-Topic Model with the 10 most relevant terms of the topics, reliability and presumed correspondence with other psycholexical models ……………………………………………………………..……….… 127 Table 4. The Seven-Topic Model with the 10 most relevant terms of the topics, reliability and presumed correspondence with other psycholexical models ……………………………………………………….… 128 Table 5. The Fourteen-Topic Model with the 10 most relevant terms of the topics, reliability and presumed correspondence with other psycholexical models ………………………………………………………… 130 Table 6. The Fifteen-Topic Model with the 10 most relevant terms of the topics, reliability and presumed correspondence with other psycholexical models …………………………………………………………. 132 Table 7. User frequency or the number of users that used the term, overall term frequency in the corpus, inverse document frequency, mean, minimum, maximum, and range …………………………..… 147xii General AbstractThis dissertation consists of three studies concerning the lexical approach of research in the field of personality, with a focus on Brazilian culture and natural language. The first study is of a theoretical nature and explores some of the criticisms regarding the lexical approach to personality research with its origin in the psychological study of natural language and crosscultural psychology, as well as methodolo...

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Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

Section: Interpretation Of the Topics: The Relevance Of The Termsmentioning

confidence: 99%

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The Personality Lexicon in Brazilian Portuguese: Studies with Natural Language

Peres¹

2018

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“…The LDA model was estimated using the MAP algorithm described by Taddy [36,37]. MAP algorithm is a variant of EM algorithm with a lower calculation cost and more stable results than the algorithms commonly used for estimates (Gibbs sampling, VEM).…”

Section: Lda Modelingmentioning

confidence: 99%

Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach (Preprint)

Abdellaoui¹,

Foulquié²,

Texier³

et al. 2017

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Background: Medication non-adherence is a major impediment to the management of many health conditions. A better understanding of the factors underlying non compliance to treatment may help health professionals to address it. Patients use social media to share their experiences regarding their treatments and their diseases. Using topic models makes it possible to model themes present in a collection of posts, thus to identify cases of non-compliance. Objective: Our study aims to detect messages describing patients' non-compliant behaviors. They are associated with a drug of interest. Thus, our objective is the clustering of posts featuring a homogeneous vocabulary related to non-adherent attitudes. Methods: We implemented a probabilistic topic model in order to identify the topics that occurred in a corpus of online messages posted between 2004 and 2013 on three of the most popular French forums. Data were collected using a Web Crawler designed by Kappa Santé as part of the Detec't project to analyze social media for drug safety. Several topics were related to non-compliance to treatment. Results: Starting from a corpus of 3 650 posts related to an antidepressant drug, (escitalopram), and 2,164 posts related to an antipsychotic drug (aripiprazole), the use of latent Dirichlet allocation allowed us to model several themes including interruptions of treatment and changes in dosage. The topic model approach detected cases of non compliance behaviors with a recall of 98.5% and a precision of 32.6%. Conclusions: Topic models enabled us to explore patients' discussions on community websites and to identify posts related with non-compliant behaviors. After a manual review of the messages in the non-compliance topics, we found that non-compliance to treatment was present in almost 6% of the posts.

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