This paper describes Humor Analysis using Ensembles of Simple Transformers, the winning submission at the Humor Analysis based on Human Annotation (HAHA) task at IberLEF 2021.

Sentiment analysis using the distilbert-base-uncased model using the movies dataset.

The official repository for the PSYCHIC model

This project classifies Internet Hinglish memes using multimodal learning. It combines text and image analysis to categorize memes by sentiment and emotion, leveraging the Memotion 3.0 dataset.

Deep learning for Natural Language Processing (FNNs, RNNs, BERT)

Fine tuning pre-trained transformer models in TensorFlow and in PyTorch for question answering

Multiclass classification on tweets about the coronavirus

This repository contains my work on the prevention and anonymization of dox content on Twitter. It contains python code and demo of the proposed solution.

Using BERT models to perform sentiment analysis on women's clothing

This app searches reddit posts and comments to determine if a product or service has a positive or negative sentiment and predicts top product mentions using Named Entity Recognition

Analyzes emotions in text chunks per chapter using a sentiment analysis model, visualizing scores across chunks as line graphs. Includes pie charts showing dominant emotions per chapter, enhancing understanding of emotional variations in text chunks. Developed using Transformers library.

This project analyzes and compares the Wikipedia articles of Xi Jinping and Vladimir Putin over 20 years, uncovering differences in portrayal, sentiment, and biases to measure public perception of each leader.

Sentiment analysis using Transformers (DistilBERT) from Hugging Face.

Performing named entity extraction task using Huggingface Transformers

Finetuning the Bert-based LLM to predict whether the tweet is toxic or not

This project is designed to streamline the recruitment process by providing a job and resume matching system and a chatbot for applicants. The key functionalities include: Job and Resume Matching and LLM powered chatbot

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.