How to Clean Text for Machine Learning with Python

You cannot go straight from raw text to fitting a machine learning or deep learning model.

You must clean your text first, which means splitting it into words and handling punctuation and case.

In fact, there is a whole suite of text preparation methods that you may need to use, and the choice of methods really depends on your natural language processing task.

In this tutorial, you will discover how you can clean and prepare your text ready for modeling with machine learning.

After completing this tutorial, you will know:

  • How to get started by developing your own very simple text cleaning tools.
  • How to take a step up and use the more sophisticated methods in the NLTK library.
  • How to prepare text when using modern text representation methods like word embeddings.

Discover how to develop deep learning models for text classification, translation, photo captioning and more in my new book, with 30 step-by-step tutorials and full source code.

Let’s get started.

  • Update Nov/2017: Fixed a code typo in the ‘split into words’ section, thanks David Comfort.
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Tutorial Overview

This tutorial is divided into 6 parts; they are:

  1. Metamorphosis by Franz Kafka
  2. Text Cleaning is Task Specific
  3. Manual Tokenization
  4. Tokenization and Cleaning with NLTK
  5. Additional Text Cleaning Considerations
  6. Tips for Cleaning Text for Word Embedding

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Metamorphosis by Franz Kafka

Let’s start off by selecting a dataset.

In this tutorial, we will use the text from the book Metamorphosis by Franz Kafka. No specific reason, other than it’s short, I like it, and you may like it too. I expect it’s one of those classics that most students have to read in school.

The full text for Metamorphosis is available for free from Project Gutenberg.

You can download the ASCII text version of the text here:

Download the file and place it in your current working directory with the file name “metamorphosis.txt“.

The file contains header and footer information that we are not interested in, specifically copyright and license information. Open the file and delete the header and footer information and save the file as “metamorphosis_clean.txt“.

The start of the clean file should look like:

One morning, when Gregor Samsa woke from troubled dreams, he found himself transformed in his bed into a horrible vermin.

The file should end with:

And, as if in confirmation of their new dreams and good intentions, as soon as they reached their destination Grete was the first to get up and stretch out her young body.

Poor Gregor…

Text Cleaning Is Task Specific

After actually getting a hold of your text data, the first step in cleaning up text data is to have a strong idea about what you’re trying to achieve, and in that context review your text to see what exactly might help.

Take a moment to look at the text. What do you notice?

Here’s what I see:

  • It’s plain text so there is no markup to parse (yay!).
  • The translation of the original German uses UK English (e.g. “travelling“).
  • The lines are artificially wrapped with new lines at about 70 characters (meh).
  • There are no obvious typos or spelling mistakes.
  • There’s punctuation like commas, apostrophes, quotes, question marks, and more.
  • There’s hyphenated descriptions like “armour-like”.
  • There’s a lot of use of the em dash (“-“) to continue sentences (maybe replace with commas?).
  • There are names (e.g. “Mr. Samsa“)
  • There does not appear to be numbers that require handling (e.g. 1999)
  • There are section markers (e.g. “II” and “III”), and we have removed the first “I”.

I’m sure there is a lot more going on to the trained eye.

We are going to look at general text cleaning steps in this tutorial.

Nevertheless, consider some possible objectives we may have when working with this text document.

For example:

  • If we were interested in developing a Kafkaesque language model, we may want to keep all of the case, quotes, and other punctuation in place.
  • If we were interested in classifying documents as “Kafka” and “Not Kafka,” maybe we would want to strip case, punctuation, and even trim words back to their stem.

Use your task as the lens by which to choose how to ready your text data.

Manual Tokenization

Text cleaning is hard, but the text we have chosen to work with is pretty clean already.

We could just write some Python code to clean it up manually, and this is a good exercise for those simple problems that you encounter. Tools like regular expressions and splitting strings can get you a long way.

1. Load Data

Let’s load the text data so that we can work with it.

The text is small and will load quickly and easily fit into memory. This will not always be the case and you may need to write code to memory map the file. Tools like NLTK (covered in the next section) will make working with large files much easier.

We can load the entire “metamorphosis_clean.txt” into memory as follows:

Running the example loads the whole file into memory ready to work with.

2. Split by Whitespace

Clean text often means a list of words or tokens that we can work with in our machine learning models.

This means converting the raw text into a list of words and saving it again.

A very simple way to do this would be to split the document by white space, including ” “, new lines, tabs and more. We can do this in Python with the split() function on the loaded string.

Running the example splits the document into a long list of words and prints the first 100 for us to review.

We can see that punctuation is preserved (e.g. “wasn’t” and “armour-like“), which is nice. We can also see that end of sentence punctuation is kept with the last word (e.g. “thought.”), which is not great.

3. Select Words

Another approach might be to use the regex model (re) and split the document into words by selecting for strings of alphanumeric characters (a-z, A-Z, 0-9 and ‘_’).

For example:

Again, running the example we can see that we get our list of words. This time, we can see that “armour-like” is now two words “armour” and “like” (fine) but contractions like “What’s” is also two words “What” and “s” (not great).

3. Split by Whitespace and Remove Punctuation

Note: This example was written for Python 3.

We may want the words, but without the punctuation like commas and quotes. We also want to keep contractions together.

One way would be to split the document into words by white space (as in “2. Split by Whitespace“), then use string translation to replace all punctuation with nothing (e.g. remove it).

Python provides a constant called string.punctuation that provides a great list of punctuation characters. For example:

Results in:

Python offers a function called translate() that will map one set of characters to another.

We can use the function maketrans() to create a mapping table. We can create an empty mapping table, but the third argument of this function allows us to list all of the characters to remove during the translation process. For example:

We can put all of this together, load the text file, split it into words by white space, then translate each word to remove the punctuation.

We can see that this has had the desired effect, mostly.

Contractions like “What’s” have become “Whats” but “armour-like” has become “armourlike“.

If you know anything about regex, then you know things can get complex from here.

4. Normalizing Case

It is common to convert all words to one case.

This means that the vocabulary will shrink in size, but some distinctions are lost (e.g. “Apple” the company vs “apple” the fruit is a commonly used example).

We can convert all words to lowercase by calling the lower() function on each word.

For example:

Running the example, we can see that all words are now lowercase.

Note

Cleaning text is really hard, problem specific, and full of tradeoffs.

Remember, simple is better.

Simpler text data, simpler models, smaller vocabularies. You can always make things more complex later to see if it results in better model skill.

Next, we’ll look at some of the tools in the NLTK library that offer more than simple string splitting.

Tokenization and Cleaning with NLTK

The Natural Language Toolkit, or NLTK for short, is a Python library written for working and modeling text.

It provides good tools for loading and cleaning text that we can use to get our data ready for working with machine learning and deep learning algorithms.

1. Install NLTK

You can install NLTK using your favorite package manager, such as pip:

After installation, you will need to install the data used with the library, including a great set of documents that you can use later for testing other tools in NLTK.

There are few ways to do this, such as from within a script:

Or from the command line:

For more help installing and setting up NLTK, see:

2. Split into Sentences

A good useful first step is to split the text into sentences.

Some modeling tasks prefer input to be in the form of paragraphs or sentences, such as word2vec. You could first split your text into sentences, split each sentence into words, then save each sentence to file, one per line.

NLTK provides the sent_tokenize() function to split text into sentences.

The example below loads the “metamorphosis_clean.txt” file into memory, splits it into sentences, and prints the first sentence.

Running the example, we can see that although the document is split into sentences, that each sentence still preserves the new line from the artificial wrap of the lines in the original document.

One morning, when Gregor Samsa woke from troubled dreams, he found
himself transformed in his bed into a horrible vermin.

3. Split into Words

NLTK provides a function called word_tokenize() for splitting strings into tokens (nominally words).

It splits tokens based on white space and punctuation. For example, commas and periods are taken as separate tokens. Contractions are split apart (e.g. “What’s” becomes “What” “‘s“). Quotes are kept, and so on.

For example:

Running the code, we can see that punctuation are now tokens that we could then decide to specifically filter out.

4. Filter Out Punctuation

We can filter out all tokens that we are not interested in, such as all standalone punctuation.

This can be done by iterating over all tokens and only keeping those tokens that are all alphabetic. Python has the function isalpha() that can be used. For example:

Running the example, you can see that not only punctuation tokens, but examples like “armour-like” and “‘s” were also filtered out.

5. Filter out Stop Words (and Pipeline)

Stop words are those words that do not contribute to the deeper meaning of the phrase.

They are the most common words such as: “the“, “a“, and “is“.

For some applications like documentation classification, it may make sense to remove stop words.

NLTK provides a list of commonly agreed upon stop words for a variety of languages, such as English. They can be loaded as follows:

You can see the full list as follows:

You can see that they are all lower case and have punctuation removed.

You could compare your tokens to the stop words and filter them out, but you must ensure that your text is prepared the same way.

Let’s demonstrate this with a small pipeline of text preparation including:

  1. Load the raw text.
  2. Split into tokens.
  3. Convert to lowercase.
  4. Remove punctuation from each token.
  5. Filter out remaining tokens that are not alphabetic.
  6. Filter out tokens that are stop words.

Running this example, we can see that in addition to all of the other transforms, stop words like “a” and “to” have been removed.

I note that we are still left with tokens like “nt“. The rabbit hole is deep; there’s always more we can do.

6. Stem Words

Stemming refers to the process of reducing each word to its root or base.

For example “fishing,” “fished,” “fisher” all reduce to the stem “fish.”

Some applications, like document classification, may benefit from stemming in order to both reduce the vocabulary and to focus on the sense or sentiment of a document rather than deeper meaning.

There are many stemming algorithms, although a popular and long-standing method is the Porter Stemming algorithm. This method is available in NLTK via the PorterStemmer class.

For example:

Running the example, you can see that words have been reduced to their stems, such as “trouble” has become “troubl“. You can also see that the stemming implementation has also reduced the tokens to lowercase, likely for internal look-ups in word tables.

You can also see that the stemming implementation has also reduced the tokens to lowercase, likely for internal look-ups in word tables.

There is a nice suite of stemming and lemmatization algorithms to choose from in NLTK, if reducing words to their root is something you need for your project.

Additional Text Cleaning Considerations

We are only getting started.

Because the source text for this tutorial was reasonably clean to begin with, we skipped many concerns of text cleaning that you may need to deal with in your own project.

Here is a short list of additional considerations when cleaning text:

  • Handling large documents and large collections of text documents that do not fit into memory.
  • Extracting text from markup like HTML, PDF, or other structured document formats.
  • Transliteration of characters from other languages into English.
  • Decoding Unicode characters into a normalized form, such as UTF8.
  • Handling of domain specific words, phrases, and acronyms.
  • Handling or removing numbers, such as dates and amounts.
  • Locating and correcting common typos and misspellings.

The list could go on.

Hopefully, you can see that getting truly clean text is impossible, that we are really doing the best we can based on the time, resources, and knowledge we have.

The idea of “clean” is really defined by the specific task or concern of your project.

A pro tip is to continually review your tokens after every transform. I have tried to show that in this tutorial and I hope you take that to heart.

Ideally, you would save a new file after each transform so that you can spend time with all of the data in the new form. Things always jump out at you when to take the time to review your data.

Have you done some text cleaning before? What are you preferred pipeline of transforms?
Let me know in the comments below.

Tips for Cleaning Text for Word Embedding

Recently, the field of natural language processing has been moving away from bag-of-word models and word encoding toward word embeddings.

The benefit of word embeddings is that they encode each word into a dense vector that captures something about its relative meaning within the training text.

This means that variations of words like case, spelling, punctuation, and so on will automatically be learned to be similar in the embedding space. In turn, this can mean that the amount of cleaning required from your text may be less and perhaps quite different to classical text cleaning.

For example, it may no-longer make sense to stem words or remove punctuation for contractions.

Tomas Mikolov is one of the developers of word2vec, a popular word embedding method. He suggests only very minimal text cleaning is required when learning a word embedding model.

Below is his response when pressed with the question about how to best prepare text data for word2vec.

There is no universal answer. It all depends on what you plan to use the vectors for. In my experience, it is usually good to disconnect (or remove) punctuation from words, and sometimes also convert all characters to lowercase. One can also replace all numbers (possibly greater than some constant) with some single token such as .

All these pre-processing steps aim to reduce the vocabulary size without removing any important content (which in some cases may not be true when you lowercase certain words, ie. ‘Bush’ is different than ‘bush’, while ‘Another’ has usually the same sense as ‘another’). The smaller the vocabulary is, the lower is the memory complexity, and the more robustly are the parameters for the words estimated. You also have to pre-process the test data in the same way.

In short, you will understand all this much better if you will run experiments.

Read the full thread on Google Groups.

Further Reading

This section provides more resources on the topic if you are looking go deeper.

Summary

In this tutorial, you discovered how to clean text or machine learning in Python.

Specifically, you learned:

  • How to get started by developing your own very simple text cleaning tools.
  • How to take a step up and use the more sophisticated methods in the NLTK library.
  • How to prepare text when using modern text representation methods like word embeddings.

Do you have any questions?
Ask your questions in the comments below and I will do my best to answer.

Do you have experience with cleaning text?
Please share your experiences in the comments below.


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97 Responses to How to Clean Text for Machine Learning with Python

  1. Alexander October 18, 2017 at 7:51 pm #

    Thank you, Jason. Very interest work.

    • Jason Brownlee October 19, 2017 at 5:35 am #

      I’m glad it helps.

      • Niwaha Barnabas March 13, 2019 at 2:53 am #

        runfile(‘C:/Users/barnabas/.spyder-py3/temp.py’, wdir=’C:/Users/barnabas/.spyder-py3′)
        theano: 1.0.3
        tensorflow: 2.0.0-alpha0
        keras: 2.2.4
        Using TensorFlow backend.

        it helps

  2. Marc October 19, 2017 at 2:22 am #

    Lemmatization is also something useful in NLTK. I recommend the course “Applied Text Mining in Python” from Coursera. Anyway, this is a good intro, thanks for it Jason

  3. Muhammed B. October 20, 2017 at 4:17 pm #

    Excellent Jason thanks a lot

  4. Vikash October 20, 2017 at 5:33 pm #

    Complete list. I also face trouble with cleaning unicodes etc at times.

    Sometimes the problem is with extract keywords from sentences. Other times it is with replacing keywords with standardised names in text. I wrote a very fast library for that called FlashText. It’s way faster than compiled regex. Here is a link to the article: https://medium.com/@vi3k6i5/search-millions-of-documents-for-thousands-of-keywords-in-a-flash-b39e5d1e126a

    And thanks again for this.

  5. Valeriy October 20, 2017 at 6:51 pm #

    Thank you very much. Visually, clear it is also very useful.
    Thanks

  6. Carlos October 23, 2017 at 3:30 am #

    Very clear and comprehensive!

    For certain applications like slot tagging tokenizing on punctuation but keeping the punctuation as tokens can be useful too.

    Another common thing to do is to trim the resulting vocabulary by just taking the top K words or removing words with low document frequency. And finally dealing with numbers! You can convert all numbers to the same token, have one token for each number of digits or drop them altogether.

    Like you said, this is all very application specific so it takes a fair amount of experimentation. Thanks again!

  7. Ahmed October 30, 2017 at 7:41 am #

    It helped me a lot especially that it not many who write in very good teaching way like yourself.
    I have question, if I want to make bag of words of text I scraped from many websites,
    I made pre processing the data and now I can get all words I need in one file but how to make the vector and assign the numbers to each web site?

  8. Will November 6, 2017 at 8:08 am #

    This is really great, thanks for this

  9. sarmad November 11, 2017 at 2:25 am #

    Jason Brownlee do you have any tutor how can I save vector into desk, every time I parse my document I have to process .

    • Jason Brownlee November 11, 2017 at 9:23 am #

      Good question, you could use pickle or save the numpy arrays directly.

  10. David Comfort November 30, 2017 at 4:01 am #

    Jason, it looks like you have a typo in the lines
    # remove all tokens that are not alphabetic
    words = [word for word in tokens if word.isalpha()]
    print(tokens[:100])

    It should be:
    # remove all tokens that are not alphabetic
    words = [word for word in tokens if word.isalpha()]
    print(words[:100])

  11. Maryam December 29, 2017 at 9:03 am #

    Thanks Jason for this great article,

    I’d be happy to know how I can remove quoted texts from a sample since these quoted texts are not originally written by my students.

    Do you also have any suggestions for removing Tables and other graphical representations?

    I’ll appreciate it,

    • Jason Brownlee December 29, 2017 at 2:36 pm #

      Perhaps some custom regex targeted to those examples?

  12. Demetri January 15, 2018 at 9:31 pm #

    Hi Jason,

    Very interesting work indeed. I was wondering if there is maybe some work you (or anyone read this) can refer me to that places punctuation in unpunctuated text.

    • Jason Brownlee January 16, 2018 at 7:33 am #

      You could learn that via a deep LSTM.

      • Demetri January 22, 2018 at 7:32 pm #

        Hi Jason,

        I have been researching deep LSTM and Matlab, and I haven’t found much useful papers/articles on punctuation insertion. Do you maybe have some papers/exercises you recommend on building a punctuation system?

        • Jason Brownlee January 23, 2018 at 7:52 am #

          No, I’d recommend starting building one directly. Start by defining a small dataset for training a model.

          • Demetri January 23, 2018 at 7:40 pm #

            Could you maybe elaborate?

            What I have now is the following:
            – Ebooks, a lot. These are test and training data (dataset.

            – Python script to remove all punctuation and capital letters. The punctuation marks with corresponding index number are
            stored in a table. This table will be used to evaluate the punctuation of unpunctuated text. I will create a new table when
            the unpunctuated text has been punctuated, and compare the two created tables.

            You said that I have to build my own deep LSTM. How would I start with this?

          • Jason Brownlee January 24, 2018 at 9:53 am #

            I’m eager to help, but I don;t have the capacity to develop this model for you or spec one.

          • Demetri January 25, 2018 at 4:50 pm #

            I have found some code that might do the trick. Will try to get it to work.

          • Jason Brownlee January 26, 2018 at 5:38 am #

            Let me know how you go.

  13. nag January 29, 2018 at 8:53 pm #

    Thanks. Its really helpful

  14. Viswajith January 30, 2018 at 5:30 am #

    Your website is extremely helpful in providing a launchpad of different ML skills. Thanks for that Jason, I have a question : when using NLTK it will eliminate the stopwords using their own corpus. If we were to do the same manually, we go about building the set of stopwords, have it in a pickle file and then eliminate them?

    • Jason Brownlee January 30, 2018 at 9:57 am #

      NLTK provides a pickle of them.

  15. Mei February 7, 2018 at 3:44 am #

    Great article. Can you give me an example code of how to remove names from the corpus? Thanks!

    • Jason Brownlee February 7, 2018 at 9:27 am #

      Sorry, I do not have an example. Perhaps you can make a list of names and remove them from the text.

  16. Shravan Singh Sisodiya March 29, 2018 at 11:58 am #

    Thank you so much for great article.

    now can i apply algorithm.

  17. sanket parate April 30, 2018 at 9:35 pm #

    How to treat with the shortcut words like bcz,u, thr etc in text mining?

    • Jason Brownlee May 1, 2018 at 5:32 am #

      It is tough.

      Two brutal approaches are:

      If you have a lot of data, model them. If you don’t remove them.

  18. Sonman May 30, 2018 at 1:14 am #

    Thank you Jason.
    I start to understand for cleaning text data.

  19. ronn June 1, 2018 at 4:07 pm #

    thank you jason for a good information…
    but i have one question…can i preprocessing (tokenize,stopword,stemming) from a file (example CSV)?..because i have a thousand document and it is saved in excel (CSV)…
    thank you

  20. Walid Saba June 23, 2018 at 8:47 am #

    You say “Stop words are those words that do not contribute to the deeper meaning of the phrase.” so (1) and (2) mean the same thing?

    (1) I ordered a pizza FOR John
    (2) I ordered a pizza WITH John

    and these also mean the same:

    (1) Mary will not take the job UNLESS it is in NY
    (2) Mary will not take the job BECAUSE it is in NY

    You know, too much “data-driven” and “machine learning” NLP is not good for you!!!!
    Reading some LOGICAL semantics – that stuff that was worked on for centuries is lacking, is my diagnosis (or, “little knowledge is dangerous”)

  21. Swapnil Patil July 17, 2018 at 5:52 pm #

    Hey jason ,

    Do you have any clue for creating meaningful sentence from tokenize words.

    Please let me know ASAP .

    Thanks in Advance.

  22. Nasir Hussain July 21, 2018 at 11:29 pm #

    thank you Jason for a good information

  23. Sunil July 24, 2018 at 7:15 pm #

    The tutorial is very helpful. Are there any online free reference pdf’s for word2vec. Please let me know. Thanks for publishing this.

  24. sunil July 25, 2018 at 2:25 am #

    hi Jason,

    Link was very helpful. Thanks for sharing. I had a question, what is the best algorithm to find if certain keywords are present in the sentence? Meaning I know what are the keywords I am looking at, problem statement here is to know whether it is present or not.

    Thanks,
    Sunil

  25. saravana July 27, 2018 at 5:36 am #

    Hi Jason
    Very nice tutorial. A couple of things that I have used furthermore are the pattern library’s spelling module as well as autocorrect. Furthermore depending on the problem statement you have, an NER filtering also can be applied (using spacy or other packages that are out there) ..

    I had a question to get your input on Topic Modelling, was curious if you recommend more steps for cleaning.

    One request I had was potentially a tutorial from you on unsupervised text for topic modelling (either for dimension reduction or for clustering using techniques like LDA etc) please 🙂

  26. GuyNa July 29, 2018 at 9:45 pm #

    i have a problem with the Stem Words part
    i can’t find a way that usa and u.s.a will be recognized as the same word

  27. nitesh July 29, 2018 at 10:20 pm #

    Does word embedding models like word2vec and gloVe deal with slang words that commonly occur in texts scraped from twitter and other messaging platforms? If not is there a way to easily make a lookup table of these slang words or is there some other method to deal with these words?

    • Jason Brownlee July 30, 2018 at 5:47 am #

      If they are in the source text and used in the same way.

  28. Nil August 2, 2018 at 12:28 am #

    Hi DR. Jason,

    Thank you for this post it is very helpful.

    I have a question, I am learning NLP on Machine Learning Mastery posts and I am trying to practice on binary classification and I have 116 negative class files and 4,396 positive class files. The doubt is, should I reduce the 4,396 positive class files to 116 in order to match the 116 negative class files? to equilibrate the number of negative class files with the number of positive class files? Or should It is not necessary to match the number of negative and positive class files?

    I hope DR. Jason may help me on this if you can.

    Best Regards

  29. Bo Peng August 15, 2018 at 2:16 pm #

    Hi Jason,

    Thanks for the post. I feel like if we are preprocessing a large batch of text inputs, running each string in the batch of strings through this whole process could be time consuming, esp. in a production product. Is there a faster way to do all of these steps in terms of computational speed?

    Thanks for the consideration.

    • Jason Brownlee August 16, 2018 at 6:01 am #

      Yes, once you have defined the vocab and the transforms, you can process new text in parallel. E.g. sentence-wise, paragraph-wise, document-wise, etc.

  30. Matthew August 17, 2018 at 11:49 pm #

    Jason,

    Thanks for this post! It is very helpful.

    I am looking to build a NLP network to group/connect scientific papers in a library that I have been compiling based on content similarity. I have achieved this already with relatively simple word parsers and Jaccard Similarity metrics. However, I think I could make my output a bit more accurate with the help of WordNet (VerbNet). That is, if these packages can handle “non-words” (i.e. industry-specific jargon) — which several of these papers contain (e.g. optogenetics, nanoparticle, etc.).

    Can these packages handle “non-words” in a way that will continue to give these words the weight/context that is reflected in the original text?

    Thanks!

  31. Zark September 10, 2018 at 8:28 pm #

    Please remove that cock crotch picture. It is disturbing
    thanks

  32. Vijayalaxmi September 28, 2018 at 12:31 pm #

    Hi Jason,

    Could you please provide the text file “metamorphosis_clean.txt’.

    • Jason Brownlee September 28, 2018 at 3:00 pm #

      You can create it from the raw text data.

      What problem are you having exactly?

  33. aniyfans October 2, 2018 at 12:37 am #

    thank you so much…..

  34. MUGDHA BHATNAGAR October 3, 2018 at 2:48 pm #

    Hey Jason,nice post!

    Can you tell me how to treat short cut words (like bcz,u,thr) in Python? What code to use?

    • Jason Brownlee October 3, 2018 at 4:15 pm #

      Perhaps try using a pre-trained word embedding that includes them?
      Perhaps remove them?
      Perhaps develop your own embedding if you have enough data?

  35. Naveen S October 15, 2018 at 3:57 pm #

    You are awesome!! I can rate 5 out of 5 for your explanation

  36. vinod December 11, 2018 at 10:55 pm #

    hi such good tutorial, i have question i have text data one big row holding these patteren data,1 product 60 values or 70 values and 100 values. after the values there is 8 empty spaces, then there is integer and text data of 10 rows. how to pivot this rows into coulmns .

    • Jason Brownlee December 12, 2018 at 5:54 am #

      Perhaps load it into memory, transform it, then save in the new format?

      Numpy can transpose using the .T attribute on the array.

  37. walid January 21, 2019 at 7:10 pm #

    HI Jason

    it was very helpful, I have a question please. I have french text and when editing it , I have the text written correctly with the “accente you know ‘é’ and ‘ô’ for instance), but when I import the text and make the first split based on space i obtain strange characters instead of these french letters, do you know what is the problem please?

    cordially
    walid

    • Jason Brownlee January 22, 2019 at 6:21 am #

      You might need to update clean text procedures in the post to correctly support unicode characters.

  38. Niket January 22, 2019 at 2:02 pm #

    How will you treat text having short cut words (like bcz u thr etc…) in text mining?
    how we can treat the above problem in R and python.

    • Jason Brownlee January 23, 2019 at 8:41 am #

      If you have enough data, you can learn how they relate to each other in the distributed representation.

      If not, perhaps a manual mapping, or drop them.

  39. usman altaf January 29, 2019 at 7:45 pm #

    for sentimental analysis data cleaning was required??? i have a data set of different comments
    how can i clean my data?

    • Jason Brownlee January 30, 2019 at 8:07 am #

      First think about the types of data cleaning that might be useful for your dataset. Then apply them.

      If you’re unsure, perhaps test a few approaches, review the output perhaps even model the output and compare the results.

  40. MJ February 7, 2019 at 1:54 pm #

    This course is incredible. I signed up for the 7 day course, and i am going to buy the book. I love this.

  41. amine February 7, 2019 at 8:22 pm #

    +1

  42. Kim July 11, 2019 at 4:39 am #

    The large ad for starting the course on ML that appears on every printed page is horrible advertising! I’ve referenced your posts many times on my twitter feed, but no more unless this is corrected! 🙁

  43. Tanuja July 18, 2019 at 5:01 pm #

    how do we extract just the important keywords from a given paragrapf?

    • Jason Brownlee July 19, 2019 at 9:08 am #

      I think this is a very hard problem.

      How do you define important?

  44. Felipe August 7, 2019 at 10:34 am #

    Thanks for post it.

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