This is a project that I written in Python for the Computational Linguistics exam, I used the nltk library, corpora must be created by your own and you must launch the script via command prompt.

An automated Hybrid Resume NER based on Rule-Based model, Machine Learning Model, and Transformer model

This project aims to classify the sentiment of tweets related to the coronavirus pandemic.

HealthCare Chatbot is an AI-powered assistant designed to provide initial medical guidance and appointment scheduling. It uses natural language processing to understand user symptoms, suggest potential conditions, and offer basic health advice.

Repository for content extraction from PDF and HTML files.

Sentiment Analysis using nlp techniques in python.

Exploring the Relationship Between UFOs, Location, Time, and Human Emotion [SQL, Python]

Here the several ways to perform sentiment analysis on text data, with varying degrees of complexity and accuracy

This project aims to build a machine learning model that can classify text messages as either spam or not spam (ham)

A smart recipe recommendation system that suggests recipes based on ingredient similarities. This project is done in PySpark

Perform sentiment analysis on the Large Movie Review Dataset using various machine learning algorithms and evaluate their performance.

Part of speech tagger implementation with Hidden Markov Model using pomegranate and NLTK libraries

This repository contains a project that demonstrates how to perform sentiment analysis on Twitter data using Apache Spark, including data preprocessing, feature engineering, model training, and evaluation.

Chatbot Using NLTK

LegalDigest - NLP Project

Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written -- referred to as natural language.

I developed a sophisticated movie recommendation system using Python, leveraging key libraries such as Pandas, NumPy, Scikit-Learn, and Natural Language Toolkit (NLTK). The system utilizes data scraping techniques to gather movie information and employs advanced data visualization techniques for insightful analysis.

The code creates a chatbot using NLTK for sentiment analysis and TF-IDF for word embedding. It reads and visualizes dialogues, performs text normalization, and employs cosine similarity to match user queries with dataset responses. The chatbot handles low-confidence cases and allows user interaction until typing 'exit.'

This is a Natural Language Processing model for Sentiment Analysis on Flipkart Dataset.

This is a Natural Language Processing model for SMS Spam Classification.