Blind-App-Reviews
awesome-sentiment-analysis
| Blind-App-Reviews | awesome-sentiment-analysis | |
|---|---|---|
| 1 | 1 | |
| 14 | 526 | |
| - | - | |
| 10.0 | 1.9 | |
| about 2 years ago | 6 months ago | |
| MIT License | - |
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Blind-App-Reviews
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[Self Promotion] I scraped Blind App and created a dataset of company reviews for over 25 companies!
You can also submit feedback and bug/feature reports on GitHub: https://github.com/HarshCasper/Blind-App-Reviews
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.
What are some alternatives?
R-text-data - List of textual data sources to be used for text mining in R
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
financial-news-dataset - Reuters and Bloomberg
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 .
wikipron - Massively multilingual pronunciation mining
Sentiment - An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
afinn - AFINN sentiment analysis in Python
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 !
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
lemmatization-lists - Machine-readable lists of lemma-token pairs in 23 languages.
n4m-sentiment - Sentiment Analysis for your MaxMSP patches - made easy.