[Topic-models] Comparison of different tools for LDA

Shayan A Tabrizi shayantabrizi at gmail.com
Fri Nov 11 05:33:02 EST 2016


Thanks all for the responses.

On Thu, Jun 9, 2016 at 7:58 PM, Ian <ian.wood at anu.edu.au> wrote:

> Hi Shayan,
>
> As Radoslaw suggests, I’d certainly have a look at recent work on topic
> models combined with word embeddings/neural networks. One particular state
> of the art (non-NN) model I’m aware of, and may be worth looking at, is Wray
> Buntines hca <https://github.com/wbuntine/topic-models>.
>
> As a first step at applied topic modelling, I’d suggest Mallet with
> hyperparameter optimisation turned on (and a preference for a larger number
> of topics) - in many cases this produces better quality topics and has a
> similar effect to non-parametric models (which choose the number of topics
> for you). I know that Gensim is able to run the Mallet models, and I’m
> pretty sure it’s doable from R as well.
>
> Hope that helps
> Best
> Ian
>
> On 9 Jun 2016, at 3:47 pm, <topic-models-request at lists.cs.princeton.edu> <
> topic-models-request at lists.cs.princeton.edu> wrote:
>
> *From: *"Kowalski, Radoslaw" <radoslaw.kowalski.14 at ucl.ac.uk>
> *Subject: **Re: [Topic-models] Comparison of different tools for LDA*
> *Date: *9 June 2016 10:08:21 am GMT+1
> *To: *Shayan A Tabrizi <shayantabrizi at gmail.com>, topic-models <
> Topic-models at lists.cs.princeton.edu>
>
>
> Hi Shayan,
>
> I would discourage you from using R because it has few robust packages for
> deep learning. My opinion is that deep learning is likely going to be used
> a lot in topic modelling in the future. In where I am in UCL we often use
> gensim but the list of relevant python packages is much longer. Gensim is
> not always a golden solution to every topic model problem. You may find
> easier to use python packages for specific problems.
>
> All the best,
> Radoslaw
>
>
> *Radoslaw Kowalski*
> PhD Student
> ______________________________
> *Consumer Data Research Centre*
> UCL Department of Political Science
> ______________________________
> T:  020 3108 1098 x51098
> E:  radoslaw.kowalski.14 at ucl.ac.uk <n.vij at ucl.ac.uk>
> W:  <http://www.cdrc.ac.uk/>www.cdrc.ac.uk
> Twitter:@CDRC_UK
> <http://www.cdrc.ac.uk/>
> ------------------------------
> *From:* topic-models-bounces at lists.cs.princeton.edu <topic-
> models-bounces at lists.cs.princeton.edu> on behalf of Shayan A Tabrizi <
> shayantabrizi at gmail.com>
> *Sent:* 08 June 2016 21:57:25
> *To:* topic-models
> *Subject:* [Topic-models] Comparison of different tools for LDA
>
> Dear Topic-Modelers,
>
> There are several tools for LDA. But I don't know which one is better and
> when? I wonder if anyone could guide me in choosing one toolbox. My
> priorities are ease-of-use and supporting various variations and extensions
> of LDA.
> Some but not all of the candidates are:
> 1- MALLET (Java)
> 2- gensim (Python)
> 3- topicmodels (R)
> 4- Stanford Topic Modeling Toolbox
>
> Thanks in advance,
> Shayan
>
>
>
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>
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