stopwords | frequent words | Natural Language Processing library
kandi X-RAY | stopwords Summary
kandi X-RAY | stopwords Summary
stopwords is a go package that removes stop words from a text content. If instructed to do so, it will remove HTML tags and parse HTML entities. The objective is to prepare a text in view to be used by natural processing algos or text comparison algorithms such as SimHash.
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Top functions reviewed by kandi - BETA
- LoadStopWordsFromString loads stop words from a string list
- Clean removes all words from the content
- Simulate hash of content
- removeStopWordsAndHash returns the hash of the given content .
- levenshteinAlgo returns the distance between a and b .
- removeStopWords remove stop words from content
- CompareSimhash returns the comparison of a and b .
- LoadStopWordsFromFile loads stop words from a file
- LevenshteinDistance returns the distance between two strings
- newFeature returns a new feature .
stopwords Key Features
stopwords Examples and Code Snippets
Lines of Code : 12License : Permissive (MIT License)
Copy@Benchmark
public String removeManually() {
String[] allWords = data.split(" ");
StringBuilder builder = new StringBuilder();
for(String word:allWords) {
if(! stopwords.contains(word)) {
builder @Benchmark
public String replaceRegex() {
return data.replaceAll(stopwordsRegex, "");
} Community Discussions
Trending Discussions on stopwords
QUESTION
As I'm working on a script to correct formatting errors from documents produced by OCR, I ran into an issue where, depending on which loop I run first, my program runs about 80% slower.
Here is a simplified version of my code. I have the following loop to check for uppercase errors (e.g., "posSible"):
...ANSWER
Answered 2021-Jun-13 at 23:19headingsFix strips out all the line endings, which you presumably did not intend. However, your question is about why changing the order of transformations results in slower execution, so I'll not discuss fixing that here.
fixUppercase is extremely inefficient at handling lines with many words. It repeatedly calls line.split() over and over again on the entire book-length string. That isn't terribly slow if each line has maybe a dozen words, but it gets extremely slow if you have one enormous line with tens of thousands of words. I found your program runs vastly faster with this change to only split each line once. (I note that I can't say whether your program is correct as it stands, just that this change should have the same behaviour while being a lot faster. I'm afraid I don't particularly understand why it's comparing each word to see if it's the same as the last word on the line.)
QUESTION
above the error pop up when importing the holoviews.I try different methods but didn't work. The following import
...ANSWER
Answered 2021-Jun-12 at 22:46Nullable is a recent addition. You need to install a newer version of Bokeh.
QUESTION
I have a dataframe containing platform terms (platform + 3 words before):
Paper A Paper B at a digital platform add a digital platform change the consumer platform got a feedback platformFor each string in the dataframe I want to delete the stopwords and any word that is occuring in front of the stop word.
Dataframe should look like this:
Paper A Paper B digital platform digital platform consumer platform feedback platformMy best try so far:
...ANSWER
Answered 2021-Jun-11 at 10:43You need to reconsider the way you deal with the word lists and the pattern you use.
Here is a possible solution with the regular re package:
QUESTION
I have a folder that contains a group of files, and each file contains a text string, periods, and commas. I want to replace the periods and commas with spaces and print all the files afterwards.
I used Replace, but this error appeared to me:
...ANSWER
Answered 2021-Jun-11 at 10:28It seems you are trying to use the string function "replace" on a list. If your intention is to use it on all of the list's members, you can do it like so:
QUESTION
I have this project.
I have a folder called "Corpus" and it contains a set of files. It is required that I delete the "stop words" from these files and then save the new files that do not contain the stop words in a new folder called "Save-files".
And when I opened the “Save-Files” folder, I saw inside it the files that I had saved, but they were without content, that is, when I open the number one file, it is empty without content.
And as it is clear in the first picture, here is the “Save-Files” folder, and inside it there is a group of files that i saved.
And when I open any of the files, it is empty.
How can I solve the problem?
...ANSWER
Answered 2021-Jun-10 at 14:10you need to update the line to read the file to
QUESTION
I'm working with quanteda package on a corpus dataframe, and here is the basic code i use :
...ANSWER
Answered 2021-Jun-10 at 12:42This is a case where knowing the value of return objects in R is the key to obtaining the result you want. Specifically, you need to know what stopwords() returns, as well as what it is expected as its first argument.
stopwords(language = "sp") returns a character vector of Spanish stopwords, using the default source = "snowball" list. (See ?stopwords for full details.)
So if you want to remove the default Spanish list plus your own words, you concatenate the returned character vector with additional elements. This is what you have done in creating all_stops.
So to remove all_stops -- and here, using the quanteda v3 suggested usage -- you simply do the following:
QUESTION
I am learning about text mining and rTweet and I am currently brainstorming on the easiest way to clean text obtained from tweets. I have been using the method recommended on this link to remove URLs, remove anything other than English letters or space, remove stopwords, remove extra whitespace, remove numbers, remove punctuations.
This method uses both gsub and tm_map() and I was wondering if it was possible to stream line the cleaning process using stringr to simply add them to a cleaning pipe line. I saw an answer in the site that recommended the following function but for some reason I am unable to run it.
...ANSWER
Answered 2021-Jun-05 at 02:52To answer your primary question, the clean_tweets() function is not working in the line "Clean <- tweets %>% clean_tweets" presumably because you are feeding it a dataframe. However, the function's internals (i.e., the str_ functions) require character vectors (strings).
I say "presumably" here because I'm not sure what your tweets object looks like, so I can't be sure. However, at least on your test data, the following solves the problem.
QUESTION
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
...ANSWER
Answered 2021-Jun-04 at 06:53It looks like some of your documents are empty, in the sense that they contain no counts of any feature.
You can remove them with:
QUESTION
There are some parts of the nltk corpus that I'd like to add to the setup.py file. I followed the response here by setting up a custom cmdclass. My setup file looks like this.
ANSWER
Answered 2021-Jun-03 at 12:13Pass the class, not its instance:
QUESTION
I am wondering if there is a way to use tokenizer(s).to_array("LOWERCASE") in the form of string instead of format uint8.
ANSWER
Answered 2021-Jun-02 at 11:28It does not seem possible with to_array to get the string token list due to Doc.to_array return type, ndarray:
Export given token attributes to a numpy
ndarray. Ifattr_idsis a sequence ofMattributes, the output array will be of shape(N, M), whereNis the length of theDoc(in tokens). Ifattr_idsis a single attribute, the output shape will be(N,). You can specify attributes by integer ID (e.g.spacy.attrs.LEMMA) or string name (e.g. “LEMMA” or “lemma”). The values will be 64-bit integers.
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