vadersentiment

JavaScript port of VADER sentiment analysis tool

Fully autonomous and intelligent platform to detect, monitor and manage potholes issues. An end-to-end system with a PWA front-end for users to report potholes and government authorities to track and manage them.

This is a collection of Jupyter notebooks that use popular sentiment analysis Python libraries to analyze text.

Sentiment Analysis of tweets about car manufacturers. Complete Docker Pipeline with ETL job that is managed through Airflow.

VaderSentiment is implementation of VADER sentiment analysis tool in Julia language.

Twitter Sentiment Analysis Using Vader Lexicon and Random Forest Classifier

Real-time financial intelligence dashboard — NLP sentiment analysis, Truth Social correlation, 12-type news classification, and ML data collection pipeline. Python · Flask · React 18

Prediction Stock price using News Sentiment and Other Major Index like S&P 500 and Nikkei 225 Index.

Analyze breaking news to track market sentiment, classify articles, and link insights to financial indicators in real time.

This Python code retrieves thousands of tweets, classifies them using TextBlob and VADER in tandem, summarizes each classification using LexRank, Luhn, LSA, and LSA with stopwords, and then ranks stopwords-scrubbed keywords per classification.

anime quotes classification and sentiment analysis

Vanilla client-side version of Python's VADER sentiment snalysis tool

VADER Sentiment Analysis Tool with C++. Valence Aware Dictionary and Sentiment Reasoner (VADER) is a lexicon and rule-based sentiment tool designed to measure sentiment of text from social media. Originally written in Python, this is a port to C++.

Text mining and sentiment analysis.

Sentiment Analysis on Twitter Tweets.

Learning streamlit as a dashboard and viz tool for data science projects

Sentiment Analysis and Word Count of Feedbacks using Apache Spark

Social Media Sentiment Analysis

Sentimental Analysis of comments on YouTube videos. Classify comments as positive, negative, or neutral. Provide visualizations to display the distribution of the sentiments.