Text-Mining | Text Mining code using TF-IDF algorithm | Topic Modeling library
kandi X-RAY | Text-Mining Summary
kandi X-RAY | Text-Mining Summary
Text Mining code using TF-IDF algorithm for finding keywords and Apriori algorithm to produce association rules
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Top functions reviewed by kandi - BETA
- Generate a frequent item set for a given candidate list
- Generate a set of candidate sets
- Find the term frequency for each document
- Calculates the word frequency of a document
- Reads the list of words and returns the cleaned list
- Calculate appriori output
- Convert a tf idf to a dictionary
- Generate association rule
- Generate a set of products from a data set
- Calculates the inverse document frequency
- Creates the input file for apriori algorithm
- Creates a list of unique words
- Create a list of stop words from a file
Text-Mining Key Features
Text-Mining Examples and Code Snippets
Community Discussions
Trending Discussions on Text-Mining
QUESTION
I'm doing some text-mining tasks and I have such a simple question and I still can't reach a conclusion.
I am applying pre-processing, such as tokenization and stemming to my training set so i can train my model.
Should I also apply this pre-processing to my test set?
...ANSWER
Answered 2021-Apr-17 at 20:38Yes, you should apply same things to your test set. Because you test set must represent your train set, that's why they should be from same distribution. Let's think intuitively:
You will enter an exam. In order you to prepare for exam and get a normal result, lecturer should ask from same subjects in the lectures. But if the lecturer ask questions from a totally different subjects that no one has seen, it is not possible to get a normal result.
QUESTION
I'm used to make some analysis from text files in Python. I usually do something like:
...ANSWER
Answered 2020-May-05 at 19:49You can iterate through the rows:
QUESTION
I am trying to apply the code provided at https://towardsdatascience.com/3-basic-distance-measurement-in-text-mining-5852becff1d7 . When I use this with my own data I seem to access a part of list that does not exist, and just not able to identify where I am making this error:
...ANSWER
Answered 2020-Apr-10 at 09:11The error in the example you provide is in the fact that transformed_results is a list with one element, holding the tokenized sentence 1.
only_event though has 2 sentences, and you are using that to provide i. So i will be 0 and 1. When i is 1, transformed_results[i] raises the error.
If you tokenize both sentences in only_event, for example with:
QUESTION
I'm trying to extract/export text from i number of standardized instances within i number of standardized .txt forms into a data frame where each instance is a separate row. I then want to export that data as an .xlsx file. So far, I can successfully extract the data (though the algorithm extracts a little more than the stated gregexpr() parameters) but can only export as .txt as a lump sum of text.
- How can I create a data frame of the extracted txt-files' text where each instance has its own row? (Once the data is in a data.frame format, I know how to export as xlsx from there.)
- How can I extract only the data from the parameters I have set?
With help (particularly from Ben from the comments of this post), here is what I have so far:
...ANSWER
Answered 2020-Mar-11 at 22:15I'm using dplyr for the convenience of the tibble object and the very effective bind_rows command:
QUESTION
I have a question relating to this old post: R Text mining - how to change texts in R data frame column into several columns with word frequencies?
I am trying to mimic something exactly similar to the one posted in link above, using R, however, with strings containing numeric characters.
Suppose res is my data frame defined by:
...ANSWER
Answered 2020-Mar-04 at 13:32You need to add the following to the freqs statement: removeNumbers = FALSE. The wfm function calls several other functions and one of them is tm::TermDocumentMatrix. In here the default supplied by wfm to this function is that removeNumbers = TRUE. So this needs to be set to FALSE.
Code:
QUESTION
In the past, I have received help with building a tf-idf for the one of my document and got an output which I wanted (please see below).
...ANSWER
Answered 2020-Jan-25 at 18:43In short, you cannot compute a tf-idf value for each feature, isolated from its document context, because each tf-idf value for a feature is specific to a document.
More specifically:
- (inverse) document frequency is one value per feature, so indexed by $j$
- term frequency is one value per term per document, so indexed by $ij$
- tf-idf is therefore indexed by $i,j$
You can see this in your example:
QUESTION
I have a list which contains some texts. So each element of the list is a text. And a text is a vector of words. So I have a list of vectors.
I am doing some text-mining on that.
Now, I'm trying to extract the words that are after the word "no". I transformed my vectors, so now they are vectors of two words. Such as :
list(c("want friend", "friend funny", "funny nice", "nice glad", "glad become", "become no", "no more", "more guys"), c("no comfort", "comfort written", "written conduct","conduct prevent", "prevent manners", "matters no", "no one", "one want", "want be", "be fired"))
My aim is to have a list of vectors which will be like :
list(c("more"), c("comfort", "one"))
So I would be able to see for a text i the vectoe of results by liste[i].
So I have a formula to extract the word after "no" (in the first vector it will be "more"). But when I have several "no" in my text it doesn't work.
Here is my code :
...ANSWER
Answered 2019-Nov-22 at 11:17In base R, we can use sapply to loop over list and grep to identify words with "no"
QUESTION
I am currently working on a project of text-mining in R, with a list of lists. I want to remove all the empty strings and the NA values of my list of lists and I haven't found a way. My data looks like this :
...ANSWER
Answered 2019-Nov-21 at 14:32you can use lapply and simple subsetting:
QUESTION
I need help with a .txt that is very unfavourable formated.
The txt is formatted like this with more than 3000 rows.
...ANSWER
Answered 2019-Aug-09 at 16:46library(dplyr)
stringr::str_split(rows,"\\||\\s+",simplify = TRUE) %>%# separate by | or white space of any length
as.data.frame() %>% # convert to dataframe so we can use dplyr
mutate(V1 = stringr::str_c(V1,V4,sep = ",")) %>% # join all words in the same row
select(-V2,-V4) %>% # drop all NNs and column 4
tidyr::separate_rows(V1,sep = ",") %>% # use separate_rows to separate rows by comma for column 1
rename(word = V1,value = V3) # rename columns
QUESTION
I'm trying to reproduce the BioGrakn example from the White Paper "Text Mined Knowledge Graphs" with the aim of building a text mined knowledge graph out of my (non-biomedical) document collection later on. Therefore, I buildt a Maven project out of the classes and the data from the textmining use case in the biograkn repo. My pom.xml looks like that:
...ANSWER
Answered 2019-Jul-23 at 13:41It may be you need to allocate more memory for your program.
If there is some bug that is causing this issue then capture a heap dump (hprof) using the HeapDumpOnOutOfMemoryError flag. (Make sure you put the command line flags in the right order: Generate java dump when OutOfMemory)
Once you have the hprof you can analyze it using Eclipse Memory Analyzer Tool It has a very nice "Leak Suspects Report" you can run at startup that will help you see what is causing the excessive memory usage. Use 'Path to GC root' on any very large objects that look like leaks to see what is keeping them alive on the heap.
If you need a second opinion on what is causing the leak check out the IBM Heap Analyzer Tool, it works very well also.
Good luck!
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Install Text-Mining
Execute textMining.py
You will be asked support and confidence value. Ones you enter those, you'll get the association rules as output.
That's pretty much it. Good Job!
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