TFIDF | TF * IDF Term Frequency Inverse Document Frequency in C # .NET | Topic Modeling library

experts,

what I want to do, as a python beginner, is to create a dendrogram with the following data:

...

I am working on a NLP problem https://www.kaggle.com/c/nlp-getting-started. I want to perform vectorization after train_test_split but when I do that, the resulting sparse matrix has size = 1 which cannot be right.

My train_x set size is (4064, 1) and after tfidf.fit_transform I get size = 1. How can that be??! Below is my code:

...

I am trying to port a sklearn feature pipeline trained in scikit-learn V0.21 to scikit-learn V0.24, because I do not have the original feature data to train the pipeline again. If I use new data, the feature dimension and position may be off from the following model, as I have DictVectorizer in the pipeline.

I've tried to use pickle and joblib to serialize the pipeline in V0.21 and then deserialize it in V0.24. Unfortunately, in both cases, the code raised ModuleNotFoundError: No module named 'sklearn.feature_extraction.dict_vectorizer' error when loading in V0.24.

I created the pipeline with the same code using V0.21 and V0.24 respectively. When printing them out, they show some minor difference.

In V0.21

...

I'm working on an automated solution to training a binary relevance multilabel classification model in Python. I'm using skmultilearn with key elements being a TFIDF vectorizer and the BinaryRelevance(MultinomialNB()) function.

I'm running into accuracy problems and need to improve the quality of my training data.

This is very labour intensive (reading or manually filtering hundreds of news articles in Excel) so I'm looking for ways to automate it. My data comes from a university database where I search for articles relevant to what I'm studying. My end goal is to assign six labels to all articles where an article can have zero, one or multiple labels. My current idea for producing training data quickly is to search the university database using criteria for each label, then tagging it to produce something that looks like this:

ID Title Full Text Label 1 Label 2 Search Criteria 0 Article 1 blahblah 1 0 Search terms associated with label 1 1 Article 2 blah 1 0 Search terms associated with label 1 2 Article 2 blah 0 1 Search terms associated with label 2 3 Article 4 balala 0 1 Search terms associated with label 2 4 Article 5 baaa 0 1 Search terms associated with label 2

Doing this will return the same article numerous times where it has multiple labels. This is shown above for article 2 which meets the search criteria for both label 1 and 2. I now need to consolidate such instances to this:

ID Title Full Text Label 1 Label 2 1 Article 2 blah 1 1

Instead of this:

ID Title Full Text Label 1 Label 2 Search Criteria 1 Article 2 blah 1 0 label 1 2 Article 2 blah 0 1 label 2

I'm very new to Python data processing. I've explored Python for the first time to explore its NLP packages. Any ideas on how to go about solving this problem? Is there some pandas dataframe functionality that I could use?

...

I'm currently working on a multilabel text classification problem, in which I have 4 labels, which is represented as 4 dummy variables. I have tried out several ways to transform the data in a way that is suitable for making the MLC.

Right now I'm running with pipelines, but as far as I can see, this doesn't fit a model with all labels included, but rather makes 1 model per label - do you agree with this?

I have tried to use MultiLabelBinarizer and LabelBinarizer, but with no luck.

Do you have a tip on how I can solve this problem in a way that makes the model include all the labels in one model, taking into account the different label combinations?

A subset of the data and my code is here:

...

I have trained a model using count_vectorizer, Tfidf_transformer and sgd classifier.

This is the tokenizer part

...

I am writing a python code using jupyter notebook that train and test a dataset in order to return a correct sentiment.

The problem that when i try to predict the sentiment of the phrase the system crash and display the below error :

ValueError: could not convert string to float: 'this book was so interstening it made me not happy'

Note i have an imbalanced dataset so i use SMOTE in order to over_sampling the dataset

code: ...

I am trying to predict toxic comments using Toxic Comment data from kaggle:

...

I have created a small example using skmultilearn trying to do multilabel text classification:

...