User messages classification using Python + Flask and Machine Learning

Setting app the application

To run the application on your computer you need to have some packages installed.

pip install flask

Also, you need to install some NLP packages as NLTK and download stopwords extension:

import nltk
nltk.download()

Data

The dataset contains 4 columns. Text is column with user messages and category is corrensponding class of the message.

Data overview and preprocessing

I made some EDA on the data:

Deleted missing values
Deleted some values from category column that doesn't make any sense.
Made some visualizations.
Checked the values distribution of category column.

Also, I preprocessed the text for training:

Removed stopwords
Lowercased all words
Changed data type to string

Model training

I tried 3 approaches to this problem

Naive Bayes Classifier for Multinomial Models Since all columns are linearly independent we are able to use Naive Bayes classifier here. Multinomial Naive Bayes classifier is a specific instance of a Naive Bayes classifier which uses a multinomial distribution for each of the features.

90% accuracy

Linear Support Vector Machine Linear Support Vector Machine is widely regarded as one of the best text classification algorithms.

86% accuracy

Logistic Regression Logistic regression is a simple and easy to understand classification algorithm, and Logistic regression can be easily generalized to multiple classes.

91% accuracy

So, I selected LG model.

Results representation with confusion matrix

App using

To test the app to need to run the app and go to the classify-text route and submit your message. The you will see a json file with {text: usertex: , category: classified category}.

andrianhevalo/TextClassifier