quanteda.textmodels | Text scaling and classification models for quanteda | Machine Learning library

by quanteda R Version: v0.9.4 License: No License

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kandi X-RAY | quanteda.textmodels Summary

quanteda.textmodels is a R library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. quanteda.textmodels has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

An R package adding text scaling models and classifiers for quanteda. Prior to quanteda v2, many of these were part of that package. Early development was supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS. For more details, see

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quanteda.textmodels has a low active ecosystem.

It has 33 star(s) with 3 fork(s). There are 8 watchers for this library.

It had no major release in the last 12 months.

There are 23 open issues and 12 have been closed. On average issues are closed in 119 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of quanteda.textmodels is v0.9.4

Quality

quanteda.textmodels has 0 bugs and 0 code smells.

Security

quanteda.textmodels has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

quanteda.textmodels code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

quanteda.textmodels does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

quanteda.textmodels releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 8946 lines of code, 0 functions and 60 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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Community Discussions

Trending Discussions on quanteda.textmodels

Identifying distinct keywords using a classifier with quanteda

Add the topic number

Measure the quality of text for text analysis after cleaning

QUESTION

Identifying distinct keywords using a classifier with quanteda

Asked 2020-Sep-14 at 15:26

I am new to quantitative text analysis, and I am attempting to extract the keywords associated with a particular classification category from the output of a naive bayes classifier. I am running the below example (classifying movie reviews as either positive or negative). I want two vectors, each containing those key words associated with either the positive and negative category respectively. Am I right in saying I should be focusing on the 'Estimated Feature Scores' from the summary() output, and if so, how do I interpret these?

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ANSWER

Answered 2020-Sep-14 at 15:26

If you just want to know the most negative and positive words, consider textstat_keyness() on a dfm created from the entire corpus, partitioned into positive and negative reviews. This does not create two word vectors, but a single word vector with a score indicating the strength of association with the negative or positive category.

Source https://stackoverflow.com/questions/63845610

QUESTION

Add the topic number

Asked 2020-Jul-03 at 16:31

From a process like this:

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ANSWER

Answered 2020-Jul-03 at 16:31

The object quant_dfm is not a data.frame, but rather an object of class dfm or document-feature matrix. Therefore, you cannot simply add a new column.

One approach might be to bind the topic proportion onto the document metadata:

Source https://stackoverflow.com/questions/62718138

QUESTION

Measure the quality of text for text analysis after cleaning

Asked 2020-Jun-19 at 09:24

Is there any measurement which could help to see if the quality of text for text analysis techniques after cleaning process has better results for example for lsa from quanteda.textmodels.

Example from the package:

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ANSWER

Answered 2020-Jun-18 at 18:00

In the upcoming version of quanteda (available on Github), textstat_summary() is added. You can use it to check how clean the texts are:

Source https://stackoverflow.com/questions/62453670

Community Discussions, Code Snippets contain sources that include Stack Exchange Network