Life & fun · September 15, 2014
Predicting what user reviews are about with LDA and gensim
Predicting what user reviews are about with LDA and gensim launched on September 15, 2014 with 22 votes and more than 75% of that year's launches. The site was still online in September 2026, 12 years on.
In plain words
This is a technical guide demonstrating topic modeling for user reviews using Latent Dirichlet Allocation and the gensim library. It is for developers and data scientists interested in extracting themes from short review text. The author compares gensim to previous tools used in academic research and explains the practical applications of automated topic prediction for managing large volumes of customer feedback.
written from the facts on this page · September 2026
From the sources
mine
I was rather impressed with the impressions and feedback I received for my Opinion phrases prototype - code repository here . So yesterday, I have decided to rewrite my previous post on topic prediction for short reviews using Latent Dirichlet Analysis and its implementation in gensim . I have previously worked with topic modeling for my MSc thesis but there I used the Semilar toolkit and a looot of C# code. Having read many articles about gensim, I was itchy to actually try it out. Why would we be interested in extracting topics from reviews? It is becoming increasingly difficult to handle the large number of opinions posted on review platforms and at the same time offer this information…from vladsandulescu.com
Does a similar job
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Usercall AI Qualitative Analysis2025 · usercall.co · ▲117If you've ever tried dumping a bunch of transcripts or surveys to chatGPT for nuanced quoted tags and themes to extract qualitative insights and patterns—you likely know the results are not great. We built a tool that makes qualitative and thematic analysis way much more nuanced and accurate than chatGPT while 10x faster than manual tagging. Try it out and let us know what you think.
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