Sentiment analysis for amazon reviews to assign whether a review is positive or negative

Spam Classification using Multinomial Naive Bayesian Classifier.

Toxic coments classificator model with BERT on pytorch

This repository is the PyTorch implementation of the Attention-Enhanced Relational Graph Convolutional Networks method for the task Multi-lingual and Cross-lingual Word-in-Context Disambiguation from SemEval-2021.

Implementation of a machine learning model to predict COVID-19 Informative Text.

Visual Genome word embeddings on region descriptions

Explores recent neural information retrieval methods (based on deep learning, transformers, BERT-like models) in order to achieve better evaluation scores than the Microblog Information Retrieval System.

I'm currently working on my bachelor's thesis which is Identifying Urban Themes in Tweets and Analyzing their Associated Sentiments. The Project aims at collecting and analyzing residents' tweets in a city, so we can provide valuable insights into how those residents feel about different aspects and issues related to the city’s quality of life, …

Finding out the political affiliation of users on Reddit from their comments, using both BERT and Doc2Vec embeddings

Machine Learning and Deep Learning approach to IR - Contextual Embeddings - Clustering Documents

Image Captioning using Recurrent Neural Networks on Flickr images with pretrained ResNet50 model features.

Data analysis, preprocessing and feature engineering of StackSample dataset & development of 2 ML models (vanilla xgboost + LLM-based model) for tag prediction

Detect whether a tweet is for Real Disaster

[PyPI] BERT Word Embeddings

Neural search engine for questions/answers from StackOverflow

Information retrieval on source code through natural language queries

Product Recommendation Engine Recommendation engines are now a one of the most common Machine Learning project that can be seen now-a-days. In fact, some biggest brands are build around one, like Netflix, Amazon, Google, etc. Thirty-five percent of purchases on Amazon come from product recommendations.

Competition to predict the intent of a text message. The data points are extracted features from text messages.

Create a knowledge graph based on Towards Data Science blogs.

A hybrid topic modeling approach fusing LDA, BERT embeddings, and autoencoders for enhanced topic extraction.