Our paper from #CoroNarrate project is out in European Politics and Society: we investigate with #stm #topicmodeling how institutional actors in Italy & Germany framed #Covid19 economic policies during the #pandemic. Thank you @tillhilmar.bsky.social Patrick Sachweh
doi.org/10.1080/2374...

Abstract: Der vorliegende Beitrag stellt einen hybriden Analyseansatz vor, der word-embedding-gestützte Topic-Modeling-Verfahren mit Distinktivitätsmaßen kombiniert, um Textgruppen zu vergleichen. Wir haben ein Topic-Modell für eine Sammlung von 600 französischen Romanen trainiert, distinktive Topics für jedes Subgenre ermittelt und diese Topics dann mit manuell erstellten textuellen Gattungsprofilen verglichen, um die Leistung verschiedener Distinktivitätsmaße bei der Identifizierung von distinktiven Topics zu evaluieren.

Macht es nicht wie ich, verpasst nicht unseren Vortrag zu BERTopics und Keyness!

Heute in der Session "Automatisierung und KI" ab 9:00 (aber unseren Vortrag erwischt ihr auch noch, wenn ihr um 10 Uhr kommt!) im Hörsaal 5.

@cnDuKeli und Julia Röttgermann […]

[Original post on fedihum.org]

NLP Applications: From Your Phone to Global Business Unlocking Our Digital World: The Real-World Natural Language Processing Applications You Use Every Day Ever wonder how your phone finishes your sentences? Or how Gmail knows that sketchy email is…

NLP Applications: From Your Phone to Global Business #machinetranslation #speechrecognition #chatbottechnology #textanalytics #whatisnlp #namedentityrecognition #applicationsofnlpinhealthcare #topicmodeling #ailanguagemodels #nlpexamples

Uncovering Kissinger: AI Text Mining & Topic Modeling

#TextMining #TopicModeling #HenryKissinger #DigitalHumanities #AIinHistory #AntConc #Mallet #HistoricalResearch #DataVisualization #AcademicResearch

Okay Bluesky, I need your help. I'm looking for some recommendations: What are your favourite tools/examples for #TopicModeling, #Stylometry, and #NetworkAnalysis?

Please comment below or DM me. And feel free to repost.

#DigitalHumanities #NLP #NLProc

Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach Background: In an era dominated by social media conversations, it is pivotal to comprehend how suicide, a critical public health issue, is discussed online. Discussions around suicide often highlight a range of topics, such as mental health challenges, relationship conflicts, and financial distress. However, certain sensitive issues, like those affecting marginalized communities, may be underrepresented in these discussions. This underrepresentation is a critical issue to investigate because it is mainly associated with underserved demographics (eg, racial and sexual minorities), and models trained on such data will underperform on such topics. Objective: The objective of this study was to bridge the gap between established psychology literature on suicidal ideation and social media data by analyzing the topics discussed online. Additionally, by generating synthetic data, we aimed to ensure that datasets used for training classifiers have high coverage of critical risk factors to address and adequately represent underrepresented or misrepresented topics. This approach enhances both the quality and diversity of the data used for detecting suicidal ideation in social media conversations. Methods: We first performed unsupervised topic modeling to analyze suicide-related data from social media and identify the most frequently discussed topics within the dataset. Next, we conducted a scoping review of established psychology literature to identify core risk factors associated with suicide. Using these identified risk factors, we then performed guided topic modeling on the social media dataset to evaluate the presence and coverage of these factors. After identifying topic biases and gaps in the dataset, we explored the use of generative large language models to create topic-diverse synthetic data for augmentation. Finally, the synthetic dataset was evaluated for readability, complexity, topic diversity, and utility in training machine learning classifiers compared to real-world datasets. Results: Our study found that several critical suicide-related topics, particularly those concerning marginalized communities and racism, were significantly underrepresented in the real-world social media data. The introduction of synthetic data, generated using GPT-3.5 Turbo, and the augmented dataset improved topic diversity. The synthetic dataset showed levels of readability and complexity comparable to those of real data. Furthermore, the incorporation of the augmented dataset in fine-tuning classifiers enhanced their ability to detect suicidal ideation, with the F1-score improving from 0.87 to 0.91 on the University of Maryland Reddit Suicidality Dataset test subset and from 0.70 to 0.90 on the synthetic test subset, demonstrating its utility in improving model accuracy for suicidal narrative detection. Conclusions: Our results demonstrate that synthetic datasets can be useful to obtain an enriched understanding of online suicide discussions as well as build more accurate machine learning models for suicidal narrative detection on social media.

JMIR Formative Res: Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach #MentalHealth #SuicidePrevention #SocialMedia #PublicHealth #TopicModeling

Visualization of the methods used as keyword clusters per abstract shows the relative share (%) of the method groups per year.

Based on an analysis of #DHd conference abstracts, I trace the evolution of #CLS methods from 2014 to 2025: from omnipresent #NetworkAnalysis and #Annotation, to the first appearance of #TopicModeling and #SentimentAnalysis, to #DeepLearning and #GenerativeAI.

Visualization of the methods used as keyword clusters per abstract shows the relative share (%) of the method groups per year.

Based on an analysis of #DHd conference abstracts, I trace the evolution of #CLS methods from 2014 to 2025: from omnipresent #NetworkAnalysis and #Annotation, to the first appearance of #TopicModeling and #SentimentAnalysis, to #DeepLearning and #GenerativeAI.

Is topic modelling obsolete? It wasn’t so long ago that topic modelling was all the rage, particularly in the digital humanities. Techniques like Latent Dirichlet Allocation (LDA), which can be used to unveil the hidden thematic ...

Is topic modelling obsolete in the age of large language models?

open.substack.com/pub/language...

#largelanguagemodels #topicmodelling #topicmodels #topicmodeling #naturallanguageprocessing #machinelearning #computationallinguistics #LLMs #digitalhumanities

Full program today at #TSU in #Tbilisi, talking about Digital Humanities, Computational Literary Studies and Mining and Modeling Text. And doing some topic modeling together on a Georgian 19th-century press corpus. I can't really tell myself but the participants […]

[Original post on fedihum.org]

Ad for virtual seminar by Dr. Kris Sankaran, Assistant Professor, Department of Statistics, University of Wisconsin-Madison, with headshot of individual with dark curly hair and glasses wearing zippered jacket and white shirt. Seminar is via Zoom: contact Cierra.streeter@vumc.org for access.

This week's Wednesday webinar (Feb. 26) at Vanderbilt Biostatistics is "Topic Models in Microbiome Analysis," at 1:30 pm CT, by Kris Sankaran @sankaranlab.bsky.social www.vumc.org/biostatistic... #RStats #GISky #GutSky #MedSky #TopicModeling

Interesse an #TopicModeling für historische Fachzeitschriften? Eike Löhden & ich haben 50 Jahrgänge der „Francia“ analysiert.

Wie haben sich Schwerpunkte über die Jahre entwickelt? Welche Unterschiede zeigen sich zwischen deutsch- und französischsprachigen […]

[Original post on fedihum.org]

Beats of Bias: Analyzing Lyrics with Topic Modeling and Gender Bias Measurements

This paper uses topic modeling and bias measurement techniques to analyze and determine gender bias in English song lyrics. Our analysis shows the thematic shift in song lyrics over the years

#mathsky #compsky #science #topicmodeling

Last update in 2024 🚀 for PsychTopics, our #Rstats #ShinyApp that automatically identifies research topics in psychology from DE, AT, CH & LUX

Data source: PSYNDEX database of @zpid.bsky.social
Method: RollingLDA #TopicModeling (aclanthology.org/2022.sdp-1.2)

👉 abitter.shinyapps.io/psychtopics

Do you work with text data? Then our ✨topiclabels✨ #Rstats package may come in handy.

Using open #LLM, it automatically assigns a topic label to a bag of words.

It also works with all popular #TopicModeling packages!

👉 cran.r-project.org/package=topi...
👉 github.com/PetersFritz/...

Happy to announce that our #Rstats package ✨topiclabels✨ has been updated on #CRAN 🎉

🤖Using open #LLMs, our package automatically assigns a topic label to a bag of words.
🤝It works with all popular #TopicModeling packages!

Find out more:
👉https://github.com/PetersFritz/topiclabels

LDA Beyond Text: Applications In Image, Music, And Graph Data LDA goes beyond text analysis, uncovering patterns in image, music, and graph data, driving innovative insights across diverse data types.

What is LDA and Why It’s Not Just for Text


Latent Dirichlet Allocation (LDA). Do you think about its roots in text analysis? #dataclustering #graphdata #imagedataanalysis #LDA #LDAapplications #LDAforimages #LDAinmusic #musicdata #nontextdata #topicmodeling
aicompetence.org/lda-beyond-t...

Topic Model Validation Methods and their Impact on Model Selection and Evaluation | Amsterdam Univer... Topic Modeling is currently one of the most widely employed unsupervised text-as-data techniques in the field of communication science. While researchers increasingly recognize the importance of valid...

🚨Exciting research alert! 🚨 Explore the world of topic modeling in communication science with our latest paper. Discover how different validation methods impact model selection and its consequences for theory development. 📚 commsky #TopicModeling www.aup-online.com/content/jour...

Thrilled to have the chance to talk about my #topicmodeling #research at the coming #python and friends conference @PyGrunn !

And a bit intimidated though, based on the website I think I m the only #female speaker 😬

#phdlife #genderbalance #programming #womenintech

Wie kann man Topics verstehen? Erläutert @u_henny anhand einer spanischen Wortwolke, basierend auf einem Korpus hispanoamerikanischer Romane. #TopicModeling #dhd2019

Definition von #TopicModeling von @JanHorstmannn: Ein auf Wahrscheinlichkeitsrechnung basierendes Verfahren zur Exploration größerer Textsammlungen. Bietet die Möglichkeit, Textsammlungen thematisch zu explorieren. https://fortext.net/routinen/methoden/topic-modeling #dhd2019

.#BSwallow & #EBayer @ #MDurrett et al @ #dayofdh18cc: #topicmodeling of Latin texts = took wks. So for comps / to fill time, they built AWESOME #FOSS app to visualize #topicmodels. cs.carleton.edu/cs_comps/1718/latin/fina... #loquela CC @dighall @nolauren...

Latent Dirichlet Allocation - under the hood

Latent Dirichlet Allocation - under the hood brooksandrew.github.io/simpleblog/articles/late... #datascience #latentdirichletallocation #topicmodeling #textmining #R