vaderSentiment
SQLAlchemy
vaderSentiment | SQLAlchemy | |
---|---|---|
20 | 124 | |
4,179 | 8,807 | |
- | 2.2% | |
0.0 | 9.7 | |
over 1 year ago | 7 days 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.
vaderSentiment
-
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
-
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.
-
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.
-
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
-
[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.
-
I made a site that tracks stock mentions and sentiment from over 180 subreddits.
C++, PHP, Javascript and vader
-
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
-
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.
-
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!:)
SQLAlchemy
-
Python: A SQLAlchemy Wrapper Component That Works With Both Flask and FastAPI Frameworks
In SQLAlchemy, models representing database tables typically subclass sqlalchemy.orm.DeclarativeBase (this class supersedes the sqlalchemy.orm.declarative_base function). Accordingly, the abstract base class in this database wrapper component is a sqlalchemy.orm.DeclarativeBase subclass, accompanied by another custom base class providing additional dunder methods.
-
Xz/liblzma: Bash-stage Obfuscation Explained
OK -
can we start considering binary files committed to a repo, even as data for tests, to be a huge red flag, and that the binary files themselves should instead be generated at testing time by source code that's stated as reviewable cleartext. This would make it much harder (though of course we can never really say "impossible") to embed a substantial payload in this way.
when binary files are part of a test suite, they are typically trying to illustrate some element of the program being tested, in this case a file that was incorrectly xz-encoded. Binary files like these weren't typed by hand, they will always ultimately come from something plaintext source.
Here's an example! My own SQLAlchemy repository has a few binary files in it! https://github.com/sqlalchemy/sqlalchemy/blob/main/test/bina... oh noes. Why are those files there? well in this case I just wanted to test that I can send large binary BLOBs into the database driver and I was lazy. This is actually pretty dumb, the two binary files here add 35K of useless crap to the source, and I could just as easily generate this binary data on the fly using a two liner that spits out random bytes. Anyone could see that two liner and know that it isn't embedding a malicious payload.
If I wanted to generate a poorly formed .xz file, I'd illustrate source code that generates random data, runs it through .xz, then applies "corruption" to it, like zeroing out the high bit of every byte. The process by which this occurs would be all reviewable in source code.
-
Introducing Flama for Robust Machine Learning APIs
Besides, flama also provides support for SQL databases via SQLAlchemy, an SQL toolkit and Object Relational Mapper that gives application developers the full power and flexibility of SQL. Finally, flama also provides support for HTTP clients to perform requests via httpx, a next generation HTTP client for Python.
-
Alembic with Async SQLAlchemy
Alembic is a lightweight database migration tool for usage with SQLAlchemy. The term migration can be a little misleading, because in this context it doesn't mean to migrate to a different database in the sense of using a different version or a different type of database. In this context, migration refers to changes to the database schema: add a new column to a table, modify the type of an existing column, create a new index, etc..
- Imperative vs. Declarative mapping style in Domain Driven Design project
-
Unlocking efficient authZ with Cerbos’ Query Plan
To simplify this process, Cerbos developers have come up with adapters for popular Object-Relational Mapping (ORM) frameworks. You can check out for more details on the query plan repo - which also contains adapters for Prisma and SQLAlchemy - as well as a fully functioning application using Mongoose as its ORM.
-
Python: Just Write SQL
That above pattern is one I've seen people do even recently, using the "select().c" attribute which from very early versions of SQLAlchemy is defined as "the columns from a subquery of the SELECT" ; this usage began raising deprecation warnings in 1.4 and is fully removed in 2.0 as it was a remnant of a much earlier version of SQLAlchemy. it will do exactly as you say, "make a subquery for each filter condition".
the moment you see SQLAlchemy doing something you see that seems "asinine", send an example to https://github.com/sqlalchemy/sqlalchemy/discussions and I will clarify what's going on, correct the usage so that the query you have is what you expect, and quite often we will add new warnings or documentation when we see people doing things we didn't anticipate.
-
A steering council note about making the global
The creator and lead maintainer of SQLAlchemy, one of the most popular and most used Python library for accessing databases (who doesn't?) gave a rather interesting response to PEP703.
If this doesn't ring any alarm bells I don't know what will.
> Basically for the moment the GIL-less idea would likely be burdensome for us and the fact that it's only an "option" seems to strongly imply major compatibility issues that we would not prefer.
https://github.com/sqlalchemy/sqlalchemy/discussions/10002#d...
-
More public SQL-queryable databases?
Recently I discovered BigQuery public datasets - just over 200 datasets available for directly querying via SQL. I think this is a great thing! I can connect these direct to an analytics platform (we use Apache Superset which uses Python SQLAlchemy under the hood) for example and just start dashboarding.
-
How useful is Python in accounting and auditing?
When using python with sql databases like postgres or mariadb or SQLite you would use SQLAlchemy or another ORM of if you're feeling brave, you code it by hand. With ORMs you provide the address of your database and it connects for you, letting you use abstractions instead of writing all the SQL yourself (kind of analogous to using vlookups or index match instead of manually entering data).
What are some alternatives?
tweets-docker-pipeline - Docker pipeline for streaming tweets and their sentiment score to a Slack channel
tortoise-orm - Familiar asyncio ORM for python, built with relations in mind
twurl - OAuth-enabled curl for the Twitter API
PonyORM - Pony Object Relational Mapper
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
Peewee - a small, expressive orm -- supports postgresql, mysql, sqlite and cockroachdb
PRAW - PRAW, an acronym for "Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
Orator - The Orator ORM provides a simple yet beautiful ActiveRecord implementation.
Docker Compose - Define and run multi-container applications with Docker
prisma-client-py - Prisma Client Python is an auto-generated and fully type-safe database client designed for ease of use
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
pyDAL - A pure Python Database Abstraction Layer