textmining | Text Mining : An App Store Reviews Exercise | Machine Learning library

by onlyphantom HTML Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | textmining Summary

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

This notebook is written as a workshop material for a session by Plug and Play Indonesia. The workshop is part of a series that serve as an introduction to data science and machine learning, and its intended audience are novices as well as junior professionals in the field of data science. Plug and Play is an accelerator for mobile startups, and as such, I have chosen to show application of text mining techniques and capabilities in processing app store reviews.

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textmining has a low active ecosystem.

It has 21 star(s) with 11 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

textmining has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of textmining is current.

Quality

textmining has 0 bugs and 0 code smells.

Security

textmining has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

textmining code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

textmining 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

textmining releases are not available. You will need to build from source code and install.

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textmining Key Features

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textmining Examples and Code Snippets

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Community Discussions

Trending Discussions on textmining

can not have consistent point size in geom_size (in ggplot)

"Wrong" TF IDF Scores

Searching for a word group with TFidfvectorizer

Stopwords Remaining in Corpus After Cleaning

QUESTION

can not have consistent point size in geom_size (in ggplot)

Asked 2022-Feb-08 at 05:11

I have two dataset and produce bubble chart with ggplot. when I want to scale the point size, they do not stay consistent. for example circle with count of 3 is bigger thant ciricle with count size of 3 in df_table1.

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ANSWER

Answered 2022-Feb-08 at 05:11

Does this approach solve your problem?

Source https://stackoverflow.com/questions/71028474

QUESTION

"Wrong" TF IDF Scores

Asked 2020-Sep-07 at 18:30

I have 1000 .txt files and planned searching for various keywords and calculate their TF-IDF Score. But for some reason the results are > 1. I did a test with 2 .txt files then: "I am studying nfc" and "You don't need AI" . For nfc and AI the TF-IDF should be 0.25 but when I open the .csv it says 1.4054651081081644.

I must admit that I did not choose the most efficient way for the code. I think the mistake is with the folders since I originally planned to check the documents by their year (annual reports from 2000-2010). But I canceled those plans and decided to check all annual reports as a whole corpus. I think the folders workaround is the problem still. I placed the 2 txt. files into the folder "-". Is there a way to make it count right?

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ANSWER

Answered 2020-Sep-07 at 18:30

I think the mistake is, that you are defining the norm as norm=None, but the norm should be l1 or l2 as specified in the documentation.

Source https://stackoverflow.com/questions/63738530

QUESTION

Searching for a word group with TFidfvectorizer

Asked 2020-Aug-15 at 01:15

I'm using sklearn to receive the TF-IDF for a given keyword list. It works fine but the only thing not working is that it doesn't count word groups such as "car manufacturers". How could I fix this? Should I use a different module ?

Pfa, the first lines of code so you see which modules I used. Thanks in advance !

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ANSWER

Answered 2020-Aug-15 at 01:15

You need to pass the ngram_range parameter in the CountVectorizer to get the result you are expecting. You can read the documentation with an example here.

https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

You can fix this like this.

Source https://stackoverflow.com/questions/63130475

QUESTION

Stopwords Remaining in Corpus After Cleaning

Asked 2020-Feb-24 at 10:25

I am attempting to remove the stopword "the" from my corpus, however not all instances are being removed.

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ANSWER

Answered 2020-Feb-24 at 10:25

Hereby reproducable code which leads to 0 instances of "the". I solved your typo and used your code from before the edit.

Source https://stackoverflow.com/questions/60300439

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

Vulnerabilities

No vulnerabilities reported

Install textmining

You can download it from GitHub.

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