JEjeffnappi·Feb 10, 2024
There's overlap, but many traditional NLP techniques are heuristics based. Here's an example: https://github.com/cjhutto/vaderSentiment
In Walmart, Delta, and Starbucks are using AI to monitor employee messagescjhutto
vaderSentiment
Python2016–2024
cjhutto / vaderSentiment
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.
6Distinct people
7Mentions
0Explicit recommenders
5.1KGitHub stars
What to know before exploring
What it is
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.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year7 dated mentions
Observed mentions: 2016-05-13 02:34:48 UTC to 2024-02-10 19:07:19 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
7 sourcesThat is correct, this was calculated using VADER sentiment analysis (https://github.com/cjhutto/vaderSentiment)
In Hacker News sentiment towards Big Tech companiesI used VaderSentiment (https://github.com/cjhutto/vaderSentiment) for sentiment analysis.
It's not a perfect solution, as I'm sure some of the discussion on these companies is loaded with jargon that wouldn't be interpreted accurately by a general sentiment analyzer.
In Hacker News sentiment towards Big Tech companiesDisclaimer: not a quant but I've built a similar system.
>some low latency twitter API that you can pay big money to access
Yes. The twitter firehose is the big one, where you get a stream of _all_ tweets. Twitter doesn't publish the price, and one source claims $360k/y for half of the hose. https://econsultancy.com/want-your-own-twitter-firehose-you-...
>And then I guess that goes to some array of GPUs to run the NLP model as quickly as possible?
General speaking, trained models usually aren't so big that you need a cluster of GPUs, or even a single one. Sometimes running it on a CPU makes more sense because if it's small enough it's just easier and cheaper.
In particular, basic NLP and sentiment analysis is pretty easy, especially like in this case when you're dealing with a very limited vocabulary. A free CPU based library like https:/
Read the rest
/github.com/cjhutto/vaderSentiment should be good enough.
> And the NLP results get traded on by placing orders with an exchange?
Yes. Doing this quickly is pretty tricky, but financial institutions like JPMorgan Chase would have access to an extremely fast and low cost (per trade) API to do this. And then they probably already have the tech to strategically place big orders, so the other bots don't realise what you're doing. Or just use a private exchange, aka a dark pool: https://en.wikipedia.org/wiki/Dark_pool
From the python, looks like it uses a series of local text file https://github.com/cjhutto/vaderSentiment
In VaderSharp – Sentiment analysis using Vader with C#Cool! I thought it was an indication of sentiment at first, I was like oh yeah la la land gets sad towards the end. Cool endeavor!
Curious if you investigated other libraries or algorithms. have you seen this - i have been playing with it, seems reliable: https://github.com/cjhutto/vaderSentiment
In Show HN: See if a movie is happy or sad with sentiment analysisI'm using VADER - https://github.com/cjhutto/vaderSentiment - because it's trained on NYT data which makes it suitable for news sentiment parsing.
The code is pretty readable but relies heavily on a ruleset which might need to be tweaked for one's need.
In Announcing SyntaxNet: The World’s Most Accurate Natural Language Parser