Reddit-Flair-Prediction-IIITD

A Web Application that inputs URLs from r/india as inputs and predicts the flair of the post based on a Machine Learning model created using the Linear Support Vector Machine Algorithm. The application is hosted on Heroku Cloud Platform[3] and could be tried at https://flairintity.herokuapp.com/ . It also has an Automated_Testing facility when POST requests are sent remotely in a text file(each .txt file should contain 1 URL).

Directory Structure

Python has been used to develop the application and several libraries have been implemented. The libraries are :

• PRAW
• SCIKITLEARN
• NUMPY
• NLTK
• PANDAS
• PICKLE
• STOPWORDS
• STRING
• RE
• FLASK

The scraped data is saved and loaded as a .CSV file and loaded as a .pkl file

The directory is a FLASK web application hosted on Heroku servers. The description of files and folders can be found below:

• Models: Folder containing the saved model.pkl file.
• Templates: Folder containing the HTML files.
• App.py: Main file responsible for starting up the Flask application.
• Procfile: File needed to host the application to Heroku.
• Requiremnets.txt:Containing all Python dependencies of the project.
• Data: Folder containing CSV files with scraped data that is pre-processed and cleaned.
• Jupyter Notebooks: Folder containing Jupyter Notebooks to collect Reddit India data, EDA and training of the Machine Learning model. Notebooks can be opened in Colaboratory by Google. Even Flair Detector and Predictors scripts are present here.

Project Workflow and Execution

The web-application allows the user to enter a r/india URL and displays the predicted flair for the submitted post.

1. Clone the repository using terminal https://github.com/Tamoghna-Sarkar/Reddit-Flair-Prediction-IIITD
2. Make sure that Python3 and pip are installed on the system.
3. Create a virtual environment:

python3 -m venv *name* 
source *name* /bin/activate 
cd *name_of_yourenv*/

4. Install all the dependencies in the root directory by executing pip install -r requirements.txt and then execute the app.py to run the FLask application on the local server.

Approach

After understanding the task in depth, and going through various open source documentations available for text processing, text analysis and suitable ML Algorithms for text classification, I based my final approach using Linear Support Vector Machine Algorithm which demonstrated optimum accuracy during the training and testing phase. I have also tried implementing the Naive Bayes, Logistic Regression, MLP Classifier and Random Forest algorithms to check the accuracy of the model with these algorithms. The test accuracies that I obtained are attached below. The approach taken for the task are as follows:

1. Collect R/India data keeping the limit as 80 posts per flair among the flairs present in R/India reddit page using the PRAW module. [1]
2. Include attributes like the flair title, body, comments, and the url of the flair.
3. Pre-process the title, comments, url and body by converting everything into format by using astype(str)
4. Remove the bad words, unnecessary symbols and STOPWORDS from all the attributes except the url by Cleaning.
5. Everything should appear in lower cases now and in a string format.
6. 5 attributes are being considered for the given task:

• Title
• Body
• URL
• Comments
• COMBINED which is an amalgamate of Title and Comments

7. The dataset is split into 70% train and 30% test data using train-test split.
8. It is nextly converted into a Vector, TD-IDF form and then the following algorithms are applied on the dataset.[2]

• Naive-Bayes
• Linear Support Vector Machine
• Logistic Regression
• Random Forest
• MLP Classifier

9. The best model created using the Linear SVM algorithm is saved and is used for prediction of the flair from the URL of the post.

After careful observations throughout the overall prediction accuracies, the model has been created on the basis of COMBINED: Title+Comments, using Linear SVM algorithm.

Flair Prediction

The saved model is loaded for predicting the flair once the post features (title, and comments) and have been cleaned, processed using NLTK. The returned result is displayed on the web-application, in this case the web-app is deployed on Heroku. An automated_testing facility is also included in app.py wherein the classifier prediction could be tested locally by just uploading a text file containing 1 R/India URL in it. All that has to be done is run the below code on your terminal:

files = {'upload_file': open('file.txt','rb')}
 r = requests.post(url, files=files)

Results

Title as Feature

Machine Learning Algorithms	Test Accuracy
Naive Bayes	0.6704
Linear SVM	0.7424
Logistic Regression	0.7272
Random Forest	0.7272
MLP	0.5227

Body as Feature

Machine Learning Algorithms	Test Accuracy
Naive Bayes	0.2765
Linear SVM	0.3257
Logistic Regression	0.3636
Random Forest	0.2651
MLP	0.2689

URL as Feature

Machine Learning Algorithms	Test Accuracy
Naive Bayes	0.2777
Linear SVM	0.3636
Logistic Regression	0.2992
Random Forest	0.2651
MLP	0.2537

Comments as Feature

Machine Learning Algorithms	Test Accuracy
Naive Bayes	0.3560
Linear SVM	0.4280
Logistic Regression	0.4356
Random Forest	0.4242
MLP	0.3219

COMBINED: Title + Comments as Feature

Machine Learning Algorithms	Test Accuracy
Naive Bayes	0.5189
Linear SVM	0.6477
Logistic Regression	0.6098
Random Forest	0.6439
MLP	0.4583

References

• [https://www.storybench.org/how-to-scrape-reddit-with-python/][1]

• [https://nlp.stanford.edu/IR-book/html/htmledition/choosing-what-kind-of-classifier-to-use-1.html][2]

• [https://blog.cambridgespark.com/deploying-a-machine-learning-model-to-the-web-725688b851c7][3]