topicModels | topics Models extension for Mallet & scikit-learn | Topic Modeling library

I have a big dataset of almost 90 columns and about 200k observations. One of the column contains descriptions, so it's only text. However, i have like 100 descriptions that are NAs.

I tried the code of Pablo Barbera from GitHub concerning Topic Models because i need it.

OUTPUT

I have a UI model called CourseUiModel that I use in my ViewModel.

In a .NET application, I am trying to construct a DTO on the repo layer as following. However, I have a nasty async function deep down in the statement. How should I chain the async calls?

I found tokens_compound() in quanteda changes the order of tokens across different R sessions. That is, the result varies every time after restarting a session even if a seed value is fixed, though it does not change in a single session.

Here is the replication procedure:

1. Find collocations, compound tokens, and save them.

I am totally beginner in programming and R. I am trying to apply the topic modelling on three literature books. I try to do it having as example Silge's and Robinson's example (Text mining with R, chapter 6), with the difference that i use no preexistent list of books but a choice of mine. I meet problems, even when i applied the given code in the example i mentioned above.

I downloaded packages (gutenbergr, tidytext, stringr, topicmodels, dplyr, tidyr) and books, and have tried to create a separate object "books" guided by the console output. I want to run the analysis by book, but i found code examples only by chapter. So i tried this:

Since I need more computational resources, I started running my R code on google collab. I have no problem with installing most of the packages I need, but for the Topicmodels package when I run the code below:

I am currently trying to combine multiple documents of a corpus into a single document using the topicmodels package. I initially imported my data through multiple csvs, each with multiple lines of text. When I import each csv, however, each line of the csv is treated as a document, and each csv is treated as a corpus. What I would like to do is merge each of the documents/lines for each csv into a single document, and then each of the csvs would represent one document in my corpus. I'm not sure if this possible--perhaps it would be easier to somehow read in all of the lines of the csv as a single text file when initially importing and then create the docs and corpus, but I don't know how to do that either. Below is the code that I have used to import my csvs:

I have 2 entities: Topic.cs, Lecture.cs, a model: TopicModel.cs and an asynchronous repo call repo.GetAllLecturesAsync(string topicId). The contents of these are intuitive.

I need to get all lectures from a repo class asynchronously and put them into a topic model. I have the following code:

Similar issues have been discussed on this forum (e.g. here and here), but I have not found the one that solves my problem, so I apologize for a seemingly similar question.

I have a set of .txt files with UTF-8 encoding (see the screenshot). I am trying to run a topic model in R using tm package. However, despite using encoding = "UTF-8" when creating the corpus, I get obvious problems with encoding. For instance, I get < U+FB01 >scal instead of fiscal, in< U+FB02>uenc instead of influence, not all punctuation is removed and some letters are unrecognizable (e.g. quotations marks are still there in some cases like view” or plan’ or ændring or orphaned quotations marks like “ and ” or zit or years—thus with a dash which should have been removed). These terms also show up in topic distribution over terms. I had some problems with encoding before, but using "encoding = "UTF-8" to create the corpus used to solve the problem. It seem like it does not help this time.

I am on Windows 10 x64, R version 3.6.0 (2019-04-26) , 0.7-7 version of tm package (all up to date). I would greatly appreciate any advice on how to address the problem.