Implementing VADER, RoBERTa and TextCNN on a twitter dataset from Kaggle

Sentiment Analysis on the Corona Tweet Dataset. Classification of tweets into classes: Positive, Negative and Neutral using various Machine Learning Models and Pre-Trained Models such as BERT and RoBERTa.

A hindi text sentiment analysis application

More and more people are exchanging text messages through the use of social media, and the analysis of the information can be used to make statistics in the behavior and in people's psychology. Using Natural Language Processing (NLP), we can extrapolate key words from each message that allow us to achieve the proposed goals.

This repository implements a question-answering system using Gradio's lower-level API, featuring two input fields for context and user questions. The system utilizes the deepset/roberta-base-squad2 model and provides a user-friendly interface for model interaction.

Este projeto utilizou a API do Twitter para coletar cerca de 50.000 tweets sobre "The Last of Us" e, em seguida, aplicou técnicas de pré-processamento de texto, Word Cloud e análise de sentimento utilizando o modelo pré-treinado Roberta.

Projekt u sklopu predmeta Obrada prirodnog jezika

For this project, machine learning algorithms are used on amazon fine food reviews dataset to analyze if the given review is a positive review or a negative review.

This repository aimed at building your own Speech classifier as well. This will enable an idea where human natural language is understood and classified in one of the category based on the context of the text spoken by the human.

An attempt at creating a chatbot utilising the Retriever-Generator approach for Open-Domain Question-Answering (QA).

This project analyzes tweets about electronic products using ReBERTa, Kafka, Logstash, Elasticsearch, and Kibana for sentiment analysis and data visualization.

This project uses the stsb-roberta-large sentence transformer model (deprecated) to check whether a set of given phrases match a certain phrase in meaning.

Best project ever

RoBERTa and XLNet models to perform classification on the human emotion.

Reproducing ACL Paper for iterative text generation and performing Robustness and Multilinguality tests

Fine Tuning text classification NLP models from huggingface with Covid-19 tweet data to build a model that classifies text based on Covid-19 sentiment

40.016 The Analytics Edge data competition code.

This project presents an innovative approach to restaurant menu optimization through sentiment analysis of customer reviews. It utilizes advanced natural language processing (NLP) techniques, employing frameworks like NLTK, RoBERTa, SpaCy, and Word2Vec, to analyze and interpret customer feedback from Yelp reviews.