#topicmodelling

[medsocionwheels]

Build and Interpret a Basic Structural Topic Model in R

New R tutorial available! Follow my 10-step process for estimating and interpreting a basic structural topic model without covariates.

Preview the Tutorial With Sound (slides with commentary) @medsocionwheels Structural topic modeling: my 10 step process for estimating and interpreting a basic structural topic model without covariates in R. Full #tutorial available on medsocionwheels.com! #TopicModeling #NLP #StructuralTopicModel #QuantitativeResearch #QualitativeResearch #ResearchMethods #R #LearnR #CodingTikTok #rstats…

[codehunter]

mutate() got multiple values for argument 'name'

I want to query createThemes to my graphql.My graphql query is:

mutation{ createTheme(name: "qwe"){ theme{ id } }}

So it errors: mutate() got multiple values for argument 'name' Can you solve and explain why its printing such error.

My code below:

from models import Theme as ThemeModel, Topic as TopicModel, Article as ArticleModel ...class CreateTheme(graphene.Mutation): class Arguments: id = graphene.Int() name = graphene.String() theme = graphene.Field(lambda: Theme) def mutate(self, name): theme = ThemeModel(name = name) theme.insert() return CreateTheme(theme = theme) ...class Mutation(graphene.ObjectType): create_theme = CreateTheme.Field() ...schema = graphene.Schema(query=Query, mutation = Mutation)

https://codehunter.cc/a/flask/mutate-got-multiple-values-for-argument-name

[ken-armbruster]

—>Draw it in #21 #aiartcommunity #aiartwork #aiartworks #artfantasy #artistsoninsta #topicmodelling #pythonprogramming #pythoncode #pythoncoding #womensfigure #womensbodybuilding #npcfigure #npcfigurecompetitor #npcfitness #ngabikinipro #naturalbodybuilding (at Cincinnati) https://www.instagram.com/p/CmUbVuzuiFc/?igshid=NGJjMDIxMWI=

[iri0m0teyamanec0]

#lda #graphicalmodeling #latentdirichletallocation #topicmodel #topicmodeling https://www.instagram.com/p/CGMp88hl9c0/?igshid=1mc15z7xdtbhw

[thatwarellp-blog]

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 A quick list of services provided by Thatware are below:

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[milovanovic]

Distributional Semantics in R: Part 2 Entity Recognition w. {openNLP}

The R code for this tutorial on Methods of Distributional Semantics in R is found in the respective GitHub repository. You will find .R, .Rmd, and .html files corresponding to each part of this tutorial (e.g. DistSemanticsBelgradeR-Part2.R, DistSemanticsBelgradeR-Part2.Rmd, and DistSemanticsBelgradeR-Part2.html, for Part 2) there. All auxiliary files are also uploaded to the repository.

Following my Methods of Distributional Semantics in R BelgradeR Meetup with Data Science Serbia, organized in Startit Center, Belgrade, 11/30/2016, several people asked me for the R code used for the analysis of William Shakespeare’s plays that was presented. I have decided to continue the development of the code that I’ve used during the Meetup in order to advance the examples that I have shown then into a more or less complete and comprehensible text-mining tutorial with {tm}, {openNLP}, and {topicmodels} in R. All files in this GitHub repository are a product of that work. 

Part 2 will introduce named entity recognition with {openNLP}, and Apache project in Java interfaced by this nice R package that, in turn, relies on {NLP} classes. We will try to make machine learning (MaxEnt models offered in {openNLP} figure out the characters from Shakespeare’s plays, a quite difficult task given that the learning algorithms at our disposal were trained on contemporary English corpora.

Figure 1.The accuracy of character recognition from Shakespeare’s comedies, tragedies, and histories; the black dashed line is the overall density. The results is not realistic (explanation given in the respective .Rmd and .hmtl files).

What I really want to show you here is how tricky and difficult it can be to do serious text-mining, and help you by exemplifying some steps that are necessary to ensure the consistency of results that you are expecting. The text-mining pipelines being developed here are in no sense perfect or complete; they are meant to demonstrate important problems and propose solutions rather than to provide a copy and paste ready chunks for future re-use. In essence, except in those cases where a standardized information extraction + text-mining pipeline is being developed (a situation where, by assumption, one periodically processes large text corpora, e.g. web-scraped news and other media reports, from various sources, in various formats, and where one simply needs to learn to live with approximations) every text-mining study will need a specific pipeline on its own. Chaining those tm_map() calls to various content_transformers from {tm} restlessly, while being ignorant of the necessary changes in parameters and different content-specific transformations - of which {tm} supports only a few - will simply not do.

Figure 2.Don’t get hooked on the results presented in the {ggplot2} figure above; {openNLP} is not that successful in recognizing personal names from Shakespeare’s plays (in spite of the fact that it works great for contemporary English documents). I have helped it a bit, by doing something that is not applicable to real-world situations; go take a look at the code from this GitHub repository.

See you soon.

[surveycircle]

Participants needed for current online survey: "Evaluation of the results from topic models" https://t.co/OUXG75XMKq via @SurveyCircle #TopicModels #Text #Visualization #MachineLearning #TextEvaluation #Evaluation pic.twitter.com/sbXTzuSBnx

— Daily Research (@daily_research) December 20, 2018

[complex-systems-science]

Cross-validation of topic modelling In my last post I finished by topic modelling a set of political blogs from 2004. I made a passing comment that it’s a challenge to know how many topics to set; the R topicmodels package doesn’t do this for you. Source: http://ift.tt/2iRMUDP

[medsocionwheels]

Watch an overview of the 10-step process on your preferred platform!

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View the Tutorial on RPubs:

Build and Interpret a Basic Structural Topic Model in R

New R tutorial available! Follow my 10-step process for estimating and interpreting a basic structural topic model without covariates.

Preview the Tutorial With Sound (slides with commentary) @medsocionwheels Structural topic modeling: my 10 step process for estimating and interpreting a basic structural topic model without covariates in R. Full #tutorial available on medsocionwheels.com! #TopicModeling #NLP #StructuralTopicModel #QuantitativeResearch #QualitativeResearch #ResearchMethods #R #LearnR #CodingTikTok #rstats…