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vaderSentiment
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, and works well on texts from other domains.
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tweets-docker-pipeline
Docker pipeline for streaming tweets and their sentiment score to a Slack channel
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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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
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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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.
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.
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.
Load the cleaned tweets and their sentiment score in a Postgres database
Store the collected tweets in a MongoDB database
A few months ago, I completed a Data Science bootcamp, where one week was all about data engineering, ETL pipelines, and workflow automation. The project for that week was to create a database of tweets that use the hashtag #OnThisDay, along with their sentiment score, and post tweets in a Slack channel to inform members about historical events that happened on that day. This pipeline had to be done with Docker Compose and included six steps:
Next, we are going to collect tweets with the hashtag #OnThisDay. To do this, first you need to create a Twitter Developer account and register an app. Follow the instructions in our reference docs to learn how to set up your Twitter app and get the necessary credentials (Consumer Key and Consumer Secret). Once you have your credentials, copy and paste them in the Credentials field of the Twitter node. Next, set the parameters:
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.