tweets-docker-pipeline
vaderSentiment
tweets-docker-pipeline | vaderSentiment | |
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2 | 20 | |
2 | 4,252 | |
- | - | |
0.0 | 0.0 | |
over 2 years ago | about 2 months ago | |
Python | Python | |
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
tweets-docker-pipeline
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Building a dockerized ETL pipeline for streaming positive tweets
First, I created an app on Twitter and got my credentials (API key and Access Token). Then, I wrote the Python code for streaming live tweets, using tweepy with my Twitter credentials. I chose to stream the hashtag #OnThisDay (thought it would be interesting to get a daily notification of what happened years ago) and collected the tweet text and user handle.
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Automate your data processing pipeline in 9 steps ⚙️
I was really excited, though also a bit overwhelmed by all the things I had to set up for this project. In total, I spent five days learning the tools, debugging, and building this pipeline with Python (including libraries like Tweepy, TextBlob, VADER, and SQLAlchemy), Postgres, MongoDB, Docker, and Airflow (most frustrating part...). If you're interested to see how I did this, you can check out the project on GitHub and read this blog post.
vaderSentiment
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Walmart, Delta, and Starbucks are using AI to monitor employee messages
There's overlap, but many traditional NLP techniques are heuristics based. Here's an example: https://github.com/cjhutto/vaderSentiment
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Turbocharge your application development using WebAssembly with SingleStoreDB
Our code uses VADER (Valence Aware Dictionary and sEntiment Reasoner). VADER is a lexicon and rule-based sentiment analysis tool that can interpret and classify emotions.
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Finding the saltiest NFL fanbase by analyzing 5 years of Reddit posts
My analyses focused on whether word usage within these threads, from 2017-2021, was positive or negative. The average level of positivity vs. negativity — often referred to as the “valence” — was scored using VADER, a language processing tool designed for online settings. Valence was averaged separately for wins and losses, then averaged again to generate a team’s overall valence score; this procedure controls for a team’s loss rate, and thus low scores do not simply reflect that a team frequently loses.
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I want to do a sentiment analysis that classifies tweets into 'positive' and 'negative' , any good resources for doing this?
I did this all as a node project but it looks like there's a python package available here - https://github.com/cjhutto/vaderSentiment
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[OC] Twitter Sentiment On Will Smith Before and After Slap
Sources: Info on VADER, VADER Dictionary (word, score, other data), VADER's special rules, original paper by the authors.
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I made a site that tracks stock mentions and sentiment from over 180 subreddits.
C++, PHP, Javascript and vader
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I made a site that tracks stock and crypto mentions and sentiment from over 180 subreddits.
vader - a sentiment analysis tool developed by researchers at Georgia Tech
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Growing up Muslim I used to get my ass kicked for being “girly”. Dad, if only you could see me now.
It's a fairly standard piece of machine learning called sentiment analysis. Human volunteers rate a corpus of texts as either positive or negative sentiment. A machine learning algorithm is then trained to predict those sentiment scores from the text itself. Sentiment analysis is widely used in comment and review moderation. If you use Python you can play around with open-source examples such as VADER yourself. While they can't always pick up on sarcasm or irony in general they do pretty well on picking up general trends.
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Thoughts/Critiques of an NLP Sentiment Analysis Project
You could try applying VADER (designed to handle social media data esp. Twitter) to tweets containing the word "apple" vs. "banana", and compare the sentiment scores.
- Amazing positivity in this community keep it up!:)
What are some alternatives?
twurl - OAuth-enabled curl for the Twitter API
redditcoins-backend - Pull reddit data from APIs and store it in local db
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
PRAW - PRAW, an acronym for "Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
Docker Compose - Define and run multi-container applications with Docker
PostgreSQL - Mirror of the official PostgreSQL GIT repository. Note that this is just a *mirror* - we don't work with pull requests on github. To contribute, please see https://wiki.postgresql.org/wiki/Submitting_a_Patch
cryptostore - A scalable storage service for cryptocurrency data
pushwasm - Utility to push a Wasm UDF into SingleStoreDB