vaderSentiment
wit-bindgen
vaderSentiment | wit-bindgen | |
---|---|---|
20 | 27 | |
4,179 | 882 | |
- | 3.5% | |
0.0 | 9.4 | |
over 1 year ago | 6 days ago | |
Python | Rust | |
MIT License | Apache License 2.0 |
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
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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!:)
wit-bindgen
- Wit-Bindgen
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WASM by Example
The component model is already shipping in Wasmtime, and will be stable for use in Node.js and in browsers via jco (https://github.com/bytecodealliance/jco) soon. WASI Preview 2 will be done in December or January, giving component model users a stable set of interfaces to use for scheduling, streams, and higher level functionality like stdio, filesystem, sockets, and http on an opt-in basis. You should look at wit-bindgen (https://github.com/bytecodealliance/wit-bindgen) to see some of the languages currently supported, and more that will be mature enough to use very soon (https://github.com/bytecodealliance/componentize-py)
Right now jco will automatically generate the JS glue code which implements a Component Model runtime on top of the JS engine's existing WebAssembly implementation. So, yes, Components are a composition of Wasm Modules and JS code is handling passing values from one module/instance to another. You still get the performance benefits of running computation in Wasm.
One day further down the standardization road, we would like to see Web engines ship a native implementation of the Component Model, which might be able to make certain optimizations that the JS implementation cannot. Until then you can consider jco a polyfill for a native implementation, and it still gives you the power to compose isolated programs written in many languages and run them in many different contexts, including the Web.
(Disclosure: I am co-chair of WASI, Wasmtime maintainer, implemented many parts of WASI/CM)
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Spin 2.0 – open-source tool for building and running WASM apps
Thank you!
To your point, the primary consideration for choosing the languages is their support for WebAssembly, and WASI in particular.
Due to Spin's heavy use of WASI and the component model, languages that have first party support in the WIT bindings generator (https://github.com/bytecodealliance/wit-bindgen) are the easiest to implement, followed by languages that can be built on top of the support for those with first party support.
For example, the JavaScript support is built by embedding QuickJS (in particular, Shopify's Javy project — https://github.com/fermyon/spin-js-sdk), which then uses the Rust SDK.
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Rust + WASM + Typescript [+ React]
There are many options, but what worked best for me is compiling with cargo-wasi and loading the resulting Wasm file with browser_wasi_shim. Using wasm32-wasi instead of wasm32-unknown-unknown requires a bit more work (the communication with JS has to be set up manually), but gives the flexibility of having just a Wasm file that can be dropped in and loaded dynamically. (There's wit-bindgen for generating wrapping code according to an interface definition but I didn't have much success with it.)
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Introducing - Wasmer Runtime 4.0
I've been playing with creating a go version of the abi for use with wit-bindgen because the current one uses cgo https://github.com/bytecodealliance/wit-bindgen
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What in Rust is equivalent to C++ DLLs (shared libraries), or what do I need to do to support extensions in my app?
wit-bindgen - Language Binding Generator for WASM Interface Type
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Quick tip: Numeromancy, WebAssembly and SingleStoreDB Cloud
wit-bindgen-rust = { git = "https://github.com/bytecodealliance/wit-bindgen.git", rev = "60e3c5b41e616fee239304d92128e117dd9be0a7" }
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Using WASM for a plugin system in Rust? (generate code at runtime and then hot reloading it as a library)
Yep, you're right. For this, there are a few options. The ones most relevant to you are fp-bindgen, which targets Wasmer, and wit-bindgen, which targets wasmtime.
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Introducing Ambient 0.1: a runtime for building high-performance multiplayer games and 3D applications, powered by Rust, WebAssembly and WebGPU
Are you evaluating if WebAssembly Component Model, its WIT format and related tooling like wit-bindgen could be a good fit for your multiple languages support?
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Using SingleStoreDB, WebAssembly and GraphQL
[package] name = "sentiment" version = "0.1.0" edition = "2021" # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html [dependencies] wit-bindgen-rust = { git = "https://github.com/bytecodealliance/wit-bindgen.git", rev = "60e3c5b41e616fee239304d92128e117dd9be0a7" } vader_sentiment = { git = "https://github.com/ckw017/vader-sentiment-rust" } lazy_static = "1.4.0" [lib] crate-type = ["cdylib"]
What are some alternatives?
tweets-docker-pipeline - Docker pipeline for streaming tweets and their sentiment score to a Slack channel
lunatic - Lunatic is an Erlang-inspired runtime for WebAssembly
twurl - OAuth-enabled curl for the Twitter API
spin - Spin is the open source developer tool for building and running serverless applications powered by WebAssembly.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
kwasm - Proof of concept React-ish UI library, powered by WebAssembly
PRAW - PRAW, an acronym for "Python Reddit API Wrapper", is a python package that allows for simple access to Reddit's API.
webassembly-tour - ⚙️ Take you through a tour of WebAssembly (WASM targets on WASI) with wasmCloud, Krustlet, WAGI, etc. 🌟 Give it a star if you like it.
Docker Compose - Define and run multi-container applications with Docker
wasi-experimental-http - Experimental outbound HTTP support for WebAssembly and WASI
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
component-model - Repository for design and specification of the Component Model