Sentiment analysis using VADER with C#

This program finds the most mentioned ticker on r/wallstreetbets and uses Vader SentimentIntensityAnalyzer to calculate the sentiment analysis.

PHP Sentiment Analyzer is a lexicon and rule-based sentiment analysis tool that is used to understand sentiments in a sentence using VADER (Valence Aware Dictionary and sentiment Reasoner).

YouTube Sentiment Analysis is a web application that analyzes the sentiment of YouTube comments, providing insights into comment sentiment using VADER sentiment analysis and interactive visualizations.

This is a crypto trading bot with Sentiment Analysis Features. It uses the Vader Lexicon to mark sentiment values for Reddit Posts. It can easily be extended to accept other data sources. Comes with Docker and Postgres. Crypto Sentiment Trading Bots use machine learning to score the sentiment a sentence and act based on it.

Sentiment Analysis of Stocks using Chat GPT and Vader (Python)

A machine learning end to end flask web app for sentiment analysis model created using Scikit-learn & VADER Sentiment.

Sentiment Analysis Python script using NLP (NLTK's VADER model) tool that analyses text data and labels them with sentiment scores.

Twitter tweets play an important role in every organisation. This project is based on analysing the English tweets and categorizing the tweets based on the sentiment and emotions of the user. The literature survey conducted showed promising results of using hybrid methodologies for sentiment and emotion analysis. Four different hybrid methodologies have been used for analysing the tweets belonging to various categories. A combination of classification and regression approaches using different deep learning models such as Bidirectional LSTM, LSTM and Convolutional neural network (CNN) are implemented to perform sentiment and behaviour analysis of the tweets. A novel approach of combining Vader and NRC lexicon is used to generate the sentiment and emotion polarity and categories. The evaluation metrics such as accuracy, mean absolute error and mean square error are used to test the performance of the model. The business use cases for the models applied here can be to understand the opinion of customers towards their business to improve their service. Contradictory to the suggestions of Google’s S/W ratio method, LSTM models performed better than using CNN models for categorical as well as regression problems.

:mortar_board: Social media text data scraping for sentiment analysis (Vader, Bert), correlation study, and Bitcoin price prediction using LSTM and XGBoost, with a comparative performance analysis across various media sources like forums and group chats.

You have been watching the Telsa stock and are deciding if you should buy some stock before close because you think it will jump up tomorrow, but you want to be more certain about your decision. This project aims to help make that decision. Vader sentiment analysis was implemented on tweets to compute a daily sentiment score. From historical stock data the difference between Tesla opening price and the prior day’s closing price was computed and used as the endogenous variable in an ARIMAX time series model with daily sentiment as an exogenous variable. This final model was able to predict that the Tesla stock will open the next day at a higher price than today’s closing price with 58.8% precision.

VADER Reddit Sentiment analysis using Dash in python

Sentiment analysis using VADER in Scala

Sentiment analysis tool using VADER in GO (GoLang)

I used Catboost for training a model on the numerical features of every YouTube video (e.g., the number of views, comments, likes, etc.) along with sentiment analysis of the video descriptions and comments using the VADER sentiment analysis model.

A CLI tool to perform simple sentiment analysis written in Rust, using a Rust port of VADER.

Application fo sentiment analysis using VADER and Support Vector Machine (SVM) with SMOTE

Sentiment Analysis in Javascript using the various lexicons including AFINN-165, VADER, NRC Word-Emotion Association, Bing and Loughran-McDonald

Power BI dashboard analyzing employee satisfaction survey results, combining structured survey data with NLP-based insights. Sentiment analysis is performed on open-ended comments using VADER’s SentimentIntensityAnalyzer, and topic modeling is conducted with BERTopic to identify recurring themes in employee feedback.

Music playlist generation capability based on a Twitter account's social media posting history by sentiment analysis to compare the distribution of tweet and song lyric sentiment polarities to generate a list of recommended songs. Integrated using Bidirectional LSTMs for Tweet Sentiment Analysis and NLTK VADER for Song Lyric Sentiment Analysis.

A comprehensive set of Jupyter notebooks that take you from NLP fundamentals to advanced techniques. Covers text preprocessing, POS tagging, NER, sentiment analysis (with VADER), text classification, word embeddings, and transformer models like BERT. Built with real-world datasets using NLTK, spaCy, scikit-learn, and Hugging Face Transformers.

Twitter Sentiment Analysis Using Vader Lexicon and Random Forest Classifier

Stock trading bot for the Alpaca API that uses VADER to perform sentiment analysis on tweets.

Sentiment analysis on BBC News headlines and descriptions using VADER. Classifies text as Positive, Negative, or Neutral based on compound scores.

Off-the-shelf sentiment analysis using Vader

Sentiment analysis of Black Mirror subtitles using VADER

Twitter Sentiment Analysis or Opinion Mining using the NLTK Vader

Automate cryptocurrency news sentiment analysis using Python. Scrape news, analyze sentiment with NLP tools (VADER/TextBlob), and visualize trends to help understand market sentiment.

This is a Python-based project that performs natural language proccessing to get sentiment analysis of Reddit comments using the Vader model and PRAW (Python Reddit API Wrapper) to get data from Reddit.

ReviewRealm utilizes NLP techniques like Vader Sentiment Analysis, NLTK, and RAKE to analyze customer reviews and provide valuable insights for smarter purchasing and marketing decisions . The backend is made using python for extra security, Jupyter notebook for creating and sharing computational documents and Mongo for databases(atlas and compass)