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What representation of chat text data should I use for user classification?


What representation of chat text data should I use for user classification?

By : user6064280
Date : November 21 2020, 11:01 PM
will be helpful for those in need You're asking what ML representation you should use for user-classification of chat text.
bag-of-words and word-vector are the main representations generally used in text-processing. However user-classification of chat is not the usual text-processing task, we look for telltale features indicative of a specific user. Here are some:
code :


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Scikit Learn Multilabel Classification: ValueError: You appear to be using a legacy multi-label data representation

Scikit Learn Multilabel Classification: ValueError: You appear to be using a legacy multi-label data representation


By : Ganesh
Date : March 29 2020, 07:55 AM
it fixes the issue i am trying to use scikit learn 0.17 with anaconda 2.7 for a multilabel classification problem. here is my code , The documents give this example:
code :
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> y = [[2, 3, 4], [2], [0, 1, 3], [0, 1, 2, 3, 4], [0, 1, 2]]
>>> MultiLabelBinarizer().fit_transform(y)
array([[0, 0, 1, 1, 1],
       [0, 0, 1, 0, 0],
       [1, 1, 0, 1, 0],
       [1, 1, 1, 1, 1],
       [1, 1, 1, 0, 0]])
SVM feature vector representation by using pre-made dictionary for text classification

SVM feature vector representation by using pre-made dictionary for text classification


By : Farha
Date : March 29 2020, 07:55 AM
it should still fix some issue In short - this is not the way it works.
The whole point of learning is to give classifier ability to assign these weights on their own. You cannot "force it" to have a high value per class for a particular feature (I mean, you could on the optimization level, but this would require changing the whole svm structure).
word2Vec vector representation for text classification algorithm

word2Vec vector representation for text classification algorithm


By : Remdore
Date : March 29 2020, 07:55 AM
With these it helps You have 2 issues in your code causing problems, both easily solved.
First, Word2Vec requires sentences to be actually a list of words, rather than an actual sentence as a single string. So from your Description_to_words just return the list, don't join.
Text Classification with scikit-learn: how to get a new document's representation from a pickle model

Text Classification with scikit-learn: how to get a new document's representation from a pickle model


By : Shoden Battousai
Date : March 29 2020, 07:55 AM
will help you As I understand from the comments, you need to access the tfidfVectorizer from inside the pipeline. This can be done easily by:
code :
tfidfVect = text_model.named_steps['vect']
tfidf_vals = tfidfVect.transform(new_document)
What Text Classification algorithms I can use to classify customer chat messages?

What Text Classification algorithms I can use to classify customer chat messages?


By : patrix
Date : March 29 2020, 07:55 AM
This might help you I think your question is quite broad, because your problem is essentially about text classification, and in literature it had been faced from most of NLP classification algorithms, so there are much more options (and maybe in your case better) than deep learning. But if you want to use deep learning you need to consider not only the architecture (simple multilayer, convolutional, LSTM, etc.), but the amount of labeled data you need for a good traning (and what about unsupervised algorithms for text classification?).
Then, independent of the approach you decide, I strongly recommend you check word embeddings algorithms (pretrained or built using your own data), specially those similar to fasttext, because will let you deal with misspelling words.
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