awesome-sentiment-analysis
afinn
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| awesome-sentiment-analysis | afinn | |
|---|---|---|
| 1 | 1 | |
| 526 | 439 | |
| - | - | |
| 1.9 | 2.6 | |
| 6 months ago | about 2 years ago | |
| Jupyter Notebook | ||
| - | Apache License 2.0 |
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awesome-sentiment-analysis
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What are the ways to handle out of domain inputs for text classification?
Get or generate negative class data. There are adversarial approaches that can improve domain generalization, but it's best to acquire more data from diverse sources. You mentioned you're working on sentiment in one of your comments- there are a ton of open-source sentiment datasets, at least for English, comprising millions of rows of data. Randomly sample from a wide variety of them to hit as many domains as possible. It's also worth including a neutral class.
afinn
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Is it possible to deploy a large NLP model for free?
I tried lighter deep learning models like DistilBERT, but they performed worse/on-par with a simple dictionary-based model like AFINN . (The use case is scoring the positivity of news headlines). Haven't looked at Spacy though.
What are some alternatives?
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
wink-eng-lite-model - English lite language model for wink-nlp.
obsei - Obsei is a low code AI powered automation tool. It can be used in various business flows like social listening, AI based alerting, brand image analysis, comparative study and more .
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
Sentiment - An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
malaya - Natural Language Toolkit for Malaysian language, https://malaya.readthedocs.io/
nlphose - Enables creation of complex NLP pipelines in seconds, for processing static files or streaming text, using a set of simple command line tools. Perform multiple operation on text like NER, Sentiment Analysis, Chunking, Language Identification, Q&A, 0-shot Classification and more by executing a single command in the terminal. Can be used as a low code or no code Natural Language Processing solution. Also works with Kubernetes and PySpark !
SuiSense - Using Artificial Intelligence to distinguish between suicidal and depressive messages (4th Place Congressional App Challenge)
n4m-sentiment - Sentiment Analysis for your MaxMSP patches - made easy.
Blind-App-Reviews - Scraped reviews of over 25 companies from the Blind App ⚡️
API-Danmark - 🇩🇰 Liste over danske API'er