Implementing VADER, RoBERTa and TextCNN on a twitter dataset from Kaggle
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Updated
May 23, 2024 - Jupyter Notebook
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
Decoding Sentiments in Amazon Feedback
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.
Finetuning Roberta on your own dataset
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.
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).
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.
Text-Shield-Offensive-Text-Detection
Flask application for sentiment detection using LLMs
This project leverages a powerful combination of NLTK, the VADER sentiment scorer, and the RoBERTa model to conduct sentiment analysis on Amazon customer reviews. It extracts actionable insights from diverse product categories, unveiling customer sentiments as positive, negative, or neutral.
Get a better understanding on Amazon reviews overall sentiment for food goods group (April, 2024)
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