algerza / reddit_comments_sarcasm_detection

Can you predict which Reddit comments are sarcastic or not?

Reddit comments sarcasm detection

Sarcasm has been part of our language for many years. It is the caustic use of irony, in which words are used to communicate the opposite of their surface meaning, in a humorous way or to mock someone or something. Understanding sarcasm is not always obvious, because it depends on your language skills and your knowledge of other people’s minds. For example, how would you classify the sentence “What a fantastic musician!”? Detecting sarcasm is much harder in text, as there are no additional cues. But what about a computer? Is it possible to train a machine learning model that can detect whether a sentence is sarcastic or not?

Problem statement

The goal of this project is to predict whether a comment is sarcastic or not based on 1 million comments scrapped from Reddit - also called sub-reddits. This means, we are facing a binary classification problem that involves incorporating NLP techniques to feed to our ML models

Results of this project

• Logistic Regression provides the best accuracy results without any data cleansing (0.73) and the fastest prediction output from the model
• State-of-the-art models became increasingly easier to use compared to some years ago. Nowadays, with few lines of code you can run these models, but the amount of training and prediction time represents their main drawback (48 hours for 20% of the original train set and an accuracy of 0.69) if it needs to be considered for production purposes

Content of this repo

1. Custom functions to load and clean the data located in the functions.py file on the notebooks folder
2. Exploratory data analysis' Jupyter notebook
3. Logistc Regression model's Jupyter notebook
4. RoBERTa model's Jupyter notebook
5. Final results for each model (csv's with their predicted labels) and summary table with a model comparison on the results folder
6. Saved our sarcasm logistic regression model to be loaded directly to predict future text's labels on the notebooks folder
7. Docker file with the logistic regression model on the docker folder

Instructions to run the model with Docker

1. Clone this repo
2. Open the terminal in the docker folder
3. Type: docker build -t dockerfile .
4. Type: docker run dockerfile
5. You will see the results in the terminal

Next steps

• Compare more models' performance (i.e. Random Forest)
• Create more datasets for RoBERTa in order to improve accuracy (i.e. comment + parent comment, clean data, etc)
• Re-organize and refactor the code, loading the already .sav trained model, predict only the latest inputs to simulate a production environment where fast and accurate output is required, store the results in a server or local directory