sentiment-analysis

LSTM for sentiment analysis and detection of domestic violence | LA Hacks 2019 (pair programming)

Spring Boot sentiment analyzer with Stanford CoreNLP, enterprise security (auth, rate limiting, XSS protection), modern UI, and production-ready configuration. Java 17 + Spring Security.

Twitter stream crawler for Bogotá mayor 2015 elections.

Interactive GUI for IBM Watson (Tone Analyzer)

A simple Android diary app that supports text, images, signatures, audio, and video in a digital diary with a security lock system, encrypted backup and recovery, and features for statistics and sentiment analysis

Polarized Text Editor using SentiWordNet 3 and Java

A program to generate training data for Stanford CoreNLP Sentiment models

LexiExp -- Free open source sentiment lexicon expansion script

Scripts for processing and annotating tweets for sentiment analysis.

Predict sentiment scores for movie reviews using CoreNLP

A secure personal journal application built with Spring Boot featuring JWT authentication, Google OAuth 2.0, sentiment analysis, Redis caching, Kafka messaging, and Swagger API documentation.

This API allows to extract information from text

The sentiment analyzer

A sentiment analysis system for smart museums that detects and visualizes visitors’ emotional responses to artworks using AI and NLP.

Full-stack journaling app built with Spring Boot, React, MongoDB, Redis, Kafka, and Gemini AI for asynchronous sentiment analysis and weekly reports.

Naive Bayes classifier with stemming and continuous learning for sentiment analysis

A feed-forward neural network driven application coded in Java which can determine if a movie review is positive or negative, and if it's positive, the corresponding movie title is added to an alphabetically sorted list.

This project provide a complet tool for Real time sentimental analysis of tweets using Kafka, Spark Streaming, CoreNLP, Elasticsearch and Kibana.

A Java based project aims to extract news articles from large .sgm file, process them and load them into MongoDB Database. It includes an Apache Spark job for word frequency analysis directly from .sgm files, and a sentiment analysis implementation using a Bag-of-Words model in Java.