Sentiment Analysis of Tweets for a renowned shoe brand

Projects I completed to become aware of the current Deep Learning practices and techniques being carried out in the field

A project to enhance ontology matching accuracy using Large Language Models (LLMs) like S-BERT.

Work area for NLP concepts

Applications of Embeddings, Machine Translation and Spam Text Classification.

A sentiment analysis in Hebrew based on HeBert (https://github.com/avichaychriqui/HeBERT) of WhatsApp groups!

The Harmony project

A project consisting of analysis of sarcasm in text using Natural Language Processing techniques. It highlights the importance of context and punctuation in sarcasm detection. Different deep learning models are applied and compared to get the best accuracy in sarcasm detection.

a repo for everyday Natural Language Processing in Python

Natural Language Processing

This project provides a simple script for determining the sentiment of a text input using TextBlob library in Python. It also returns the most positive and most negative sentence in the input text. The script can be used as a standalone tool or integrated into other projects.

This repository contains code for generating blog content using the LLama 2 language model. It integrates with Streamlit for easy user interaction. Simply input your blog topic, desired word count, and writing style to generate engaging blog content.

In this project our goal is to acheive the problem of converting audio data into textual data.

A basic chatterbox Titu who talks about Analytics.

NLP Updates list of Experiences, Resources & Best Practices.

This project provides a comprehensive analysis of tweet sentiment during the COVID-19 pandemic using Natural Language Processing (NLP) techniques. The dataset is classified into five sentiment classes, and thorough data exploration and preprocessing techniques are applied to handle imbalances. ML & DL models utilised.

"Detect sarcasm effortlessly! This Python app uses NLP and ML to analyze text sentiment, distinguishing sarcastic tones. With a user-friendly interface, input any text for real-time sarcasm identification. Achieve accurate results through advanced sentiment analysis techniques and trained models."

Experience simple Text-to-Speech (TTS) functionality using pyttsx3 module, compatible with various speech engines like NSSpeechSynthesizer, SAPI5, and eSpeak.