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Proyecto donde actualizaré modelos de analisis de texto y estructura musical para la clasificación de canciones por su sentimiento

Sentiment analysis based movie platform

Sentiment Analysis CLI Tool to analyze text files. Based on the "siebert/sentiment-roberta-large-english" model. Prints out whether text content is "positive" or "negative". (👍🏼 or 👎🏼)

Repository with all what is necessary for sentiment analysis and related areas

An easy-to-use sentiment analysis tool using TextBlob and a custom GUI. Quickly assess text sentiment as positive, negative, or neutral. Ideal for social media monitoring, customer feedback analysis, and content moderation. Open-source under the MIT License.

Multi-classification Sentiment analysis on Web-Scraped Amazon product reviews using NLP (with nltk, TextBlob, VADER) along with various ML and DL (RoBERTa) models.

Sentiment analysis with Python as a final project on Coursera

This repository contains code for sentiment analysis of movie reviews.IMDB dataset is used.

A system that analyzes the tone of texts and statements.

This project is a data analysis (data science) tool with scripts to analyze images and text data and store results in a Microsoft Access database.This project also includes a set of Power BI dashboards that are connected to the database and provide a visual representation of the analyzed data.

Sentiment analysis for Twitter and social media

Sentiment Classifier for Japanese texts using BERT

A web app that classifies sentiment in Disneyland reviews

Sentiment Analysis of User Reviews

Dataset for training Sentiment analysis models

Sentiment Analysis using BERT embeddings + MLP (Multi Layer Perceptron). Served as A REST API in FastAPI and running in Docker.

Implementation of a sentiment classifier using varius NLP techniques and models.

Review sentiment analyzer trained on Amazon books reviews

This project focuses on sentiment analysis. Social Sentiment analysis is the use of natural language processing (NLP) to analyze social conversations online and determine deeper context as they apply to a topic, brand or theme.