#topicmodelling

[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=

[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

[iri0m0teyamanec0]

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

[thatwarellp-blog]

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Mr. Banik is actively helping over 300+ clients with his strategies in providing effective solutions for every challenge.

 A quick list of services provided by Thatware are below:

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·         Semantic SEO and information retrieval: Artifitial intelligence is the future and it would lead the digital industry majorly in the coming decade.Thatware has explored this opportunity and made it the ruling concept and theory behind its service delivery.Thatware explores concepts like Cosine similarity,Markov chain,Latent Dirichlet Allocation (LDA),Probabilistic Latent Semantic Analysis,Jacardindex,Cohen’sKappa,Topicmodeling,vector space modeling,Link intersect using R,Rocchioalgorithm,Bag of words,Best matches correlation,Hierarchialclustering,Documentheatmap,sentimentanalysis,Document v/s document similarity,anchor-text similarity,co-occurrence,K-Mean clustering,Flatclustering,NaïveBayes,Predictive analysis using Markov chain,Semanticproximity,Adaboostalgorithm,Fuzzyclustering,Prediction of trends,Learning-vector-quantization,TF-IDF,Precision,Recall,F-Measure,Champion list,ManualCora.To know more about these concepts navigate directly to Thatware website or get in touch with us for in depth analysis.

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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…