This repository showcases my work on sarcasm detection task.
Problem description: Given raw comments from Reddit, we have to classify them as sarcastic or not.
Dataset source: https://www.kaggle.com/danofer/sarcasm
Paper referred: https://arxiv.org/abs/1610.08815

1. prepare-data-csv.ipynb: Using raw data source to create usable data CSVs
2. Data cleaning and EDA.ipyb: Cleaning text and some Exploratory data analysis on our data
3. Modelling.ipynb: CNN model for text classification task

We have a prepared a perfectly balanced dataset for our task.

We've used 1D CNN models to extract features from raw texts and make classifications. We've used combinations of three different kinds of features:

1. Content features from raw texts
2. Sentiment features using Transfer Learning. Model trained on twitter dataset. More information can be found here: https://github.com/NamanJain2050/semeval-2014-task-9/
3. Emotion features using Transfer Learning. Two models trained on two different datasets. More information can be found here: https://github.com/NamanJain2050/emotion-detection

Predictions made using only content features extracted from 1D CNN. Model architecture is as follows:

Predictions made using content features extracted from 1D CNN and sentiment features from pre-trained model. Model architecture is as follows:

Predictions made using content features extracted from 1D CNN and emotion features from pre-trained model. Model architecture is as follows:

Predictions made using content features extracted from 1D CNN and emotion features from pre-trained model. This time we'll use a different model trained for emotion features. Model architecture is as follows:

Predictions made using content features extracted from 1D CNN, sentiment features and emotion features extracted from pre-trained models. Model architecture is as follows:

Predictions made using content features extracted from 1D CNN, sentiment features and emotion features extracted from pre-trained models. Model architecture is as follows:

We've seen that adding emotion and sentiment features from pretrained models have degraded our results.
Possible reason(s):

1. Models were trained on much smaller datasets as compared to our SARC dataset