Tweet Sentiment Word Cloud is a tool for Analyzing twitter tweets for a keyword and distinguishing sentiment by positive, negative and neutral. And finding most frequently used word to determine sentiments.
You can check out the Tweet Sentiment Word Cloud from below 👇
- The project divides the tweet into three different sentiments and create a wordcloud among them with most frequent used words.
- The project was submitted in OpenHacks 2020
- Python - Python is an interpreted, object-oriented, high-level programming language with dynamic semantics
- Flask - Flask is a popular, extensible web microframework for building web applications with Python.
- Natural Language Toolkit - NLTK is a leading platform for building Python programs to work with human language data.
- React - A JavaScript library for building user interfaces
- react-tagcloud - Simple and extensible tag/word cloud React component.
- Tweepy - An easy-to-use Python library for accessing the Twitter API.
- VADER-Sentiment-Analysis - VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media.
Thanks to
- Novel COVID API for open-source API.
- Ranjan Bir Bajracharya (Team Leader)
- Debaleen Das Spandan (Contributer)
- Shouvit Pradhan (Contributer)
- Bijayata Shrestha (Contributer)
MIT License
Copyright (c) 2020 Ranjan Bir Bajracharya and Debaleen Das Spandan and Shouvit Pradhan and Bijayata Shrestha
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