A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Egger, Roman; Yu, Jing

doi:10.3389/fsoc.2022.886498

Cited by 276 publications

(161 citation statements)

References 59 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…Specifically, we looked at the frequency of post publishing. Then, we analyzed the thematic composition of these posts using BERTopic (Grootendorst, 2022), a novel topic modeling technique that supports dynamic topic modeling for multilingual text corpora and demonstrates high performance across different domains (Egger & Yu, 2022). We chose topic modeling for the analysis of frames since this method offers an advantage of mining emerging frames from a body of texts rather than imposing pre-determined classification categories on the data set, and is thus especially useful in fast-changing and previously unexplored contexts (Groshek & Engelbert, 2013) such as the one we focus on.…”

Section: Data Analysis Rq1: Representation Of the War In Ukraine Thro...mentioning

confidence: 99%

My war is your special operation: Engagement with pro- and anti-regime framing of the war in Ukraine on Russian social media

Urman¹,

Makhortykh²

2022

Preprint

View full text Add to dashboard Cite

On 24 February 2022, Russia launched a large-scale invasion of Ukraine which since then has been consistently referred to by the Russian authorities as a special operation and not war. The purpose of this specific framing of the Russian aggression can be attributed to multiple reasons, but among the main advantages it provides to the Kremlin is the emphasis of power inequality between Russia and Ukraine as well as minimizing the concerns of the Russian population about the impact of the war on their lives. However, despite all the Kremlin’s propaganda and censorship efforts, it is still unclear what the dominant framing of the war is among regular Russian internet users and how they engage with different frames used in relation to it. To address this gap, we conducted an automated analysis of over 6 million public posts from VK, the biggest Russian social media platform, published between 14 February 2022 and 24 June 2022 and related to different ways of framing the war as well as the ways users engage with these frames. Our analysis reveals that the invasion was predominantly discussed in connection with the losses in the Russian army, and allusions to WW2 were also common. The users who posted about the war were older than average VK users and tuned in to pro-regime and pro-war content more frequently.

show abstract

Section: Data Analysis Rq1: Representation Of the War In Ukraine Thro...mentioning

confidence: 99%

My war is your special operation: Engagement with pro- and anti-regime framing of the war in Ukraine on Russian social media

Urman¹,

Makhortykh²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…O NMF usa uma abordagem de álgebra linear para extrac ¸ão de tópicos. [Egger and Yu 2022] avaliam e comparam o desempenho de quatro técnicas de modelagem de tópicos: LDA, NMF, Top2Vec e BERTopic. Além dos métodos já citados anteriormente, o Top2Vec usa embeddings para representar os textos curtos.…”

Section: Trabalhos Relacionadosunclassified

“…Ao analisar os trabalhos apresentados nessa sec ¸ão, percebemos que alguns autores elaboraram um estudo exploratório de métodos específicos para textos curtos [Qiang et al 2020, Costa andDuarte 2019], enquanto outros focaram em avaliar o desempenho de métodos de modelagem de tópicos para textos genéricos, mas aplicados no contexto de textos curtos [Albalawi et al 2020]. Por fim, as abordagens apresentadas verificaram o desempenho apenas de métodos de textos genéricos com e sem o uso de embeddings [Egger and Yu 2022]. Nenhum dos trabalhos relacionados explorou o que este artigo propõe: uma análise comparativa entre métodos tradicionais (LDA), modelos para textos curtos (GSDMM e PTM) e novas abordagens para textos genéricos com o uso de embeddings (BERTopic).…”

Section: Trabalhos Relacionadosunclassified

“…No entanto, métodos tradicionais de modelagem de tópicos enfrentam dificuldades ao tentar identificar os assuntos tratados em textos curtos, principalmente pela falta de coocorrência de palavras [Costa and Duarte 2019]. Mais recentemente, novas abordagens foram desenvolvidas que se apóiam na semântica distribucional e na utilizac ¸ão de vetores densos de palavras para compor tópicos [Egger and Yu 2022]. Entretanto, problemas como a falta de coocorrência de palavras, ausência de informac ¸ões nos textos e o uso de termos inseridos apenas no contexto das redes sociais ainda se mostram como desafios [Qiang et al 2020].…”

Section: Introduc ¸ãOunclassified

See 1 more Smart Citation

Modelagem de Tópicos em Textos Curtos: uma Avaliação Experimental

Amorim

Murrugarra-Llerena

Silva

et al. 2022

Anais Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD 2022)

View full text Add to dashboard Cite

As redes sociais são utilizadas para expressar opiniões ou interagir com outras pessoas. Diante do amplo escopo de assuntos publicados e a linguagem informal presente nas postagens, a busca de informações é significativamente desafiadora. Assim, descobrir automaticamente os tópicos tratados nos textos ruidosos e com pouco contexto postados é primordial. Dado este cenário, este artigo contribui com uma análise comparativa de métodos de modelagem de tópicos, incluindo os baseados em abordagens probabilísticas e neurais. Ademais, esse artigo contribui com um método para rotular automaticamente os tópicos, permitindo uma análise qualitativa dos tópicos descobertos.

show abstract

“…With that, it can automatically detect topics present in the text and generate jointly embedded topic, document, and word vectors. There are studies that compared LDA and Top2Vec (Ma et al, 2021 ; Egger and Yu, 2022 ). They reported that Top2Vec produced qualitatively higher quality results than LDA.…”

Section: Introductionmentioning

confidence: 99%

Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis

Karas

Qu²,

Xu³

et al. 2022

Front. Artif. Intell.

View full text Add to dashboard Cite

Social media has become an important resource for discussing, sharing, and seeking information pertinent to rare diseases by patients and their families, given the low prevalence in the extraordinarily sparse populations. In our previous study, we identified prevalent topics from Reddit via topic modeling for cystic fibrosis (CF). While we were able to derive/access concerns/needs/questions of patients with CF, we observed challenges and issues with the traditional techniques of topic modeling, e.g., Latent Dirichlet Allocation (LDA), for fulfilling the task of topic extraction. Thus, here we present our experiments to extend the previous study with an aim of improving the performance of topic modeling, by experimenting with LDA model optimization and examination of the Top2Vec model with different embedding models. With the demonstrated results with higher coherence and qualitatively higher human readability of derived topics, we implemented the Top2Vec model with doc2vec as the embedding model as our final model to extract topics from a subreddit of CF (“r/CysticFibrosis”) and proposed to expand its use with other types of social media data for other rare diseases for better assessing patients' needs with social media data.

show abstract

A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts

Cited by 276 publications

References 59 publications

My war is your special operation: Engagement with pro- and anti-regime framing of the war in Ukraine on Russian social media

My war is your special operation: Engagement with pro- and anti-regime framing of the war in Ukraine on Russian social media

Modelagem de Tópicos em Textos Curtos: uma Avaliação Experimental

Experiments with LDA and Top2Vec for embedded topic discovery on social media data—A case study of cystic fibrosis

Contact Info

Product

Resources

About