awesome-sentiment-analysis
lemmatization-lists
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| awesome-sentiment-analysis | lemmatization-lists | |
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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.
lemmatization-lists
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Ambiguous spellings
It's a bit of a massive undertaking maintaining such a data set so it's mostly taken from https://github.com/michmech/lemmatization-lists At the top of the file you'll see some additional I've added to deal with personal pronouns and numbers.
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Is there a text list of words and their variations?
Another one to add to your list: https://github.com/michmech/lemmatization-lists
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Trying to build a lemmatizer from scratch
One approach might be to take a lemmatization list, like the lemma-token lists at https://github.com/michmech/lemmatization-lists/, and compile it into a Finite State Transducer. The Helsinki FST package, for instance, has an hfst-strings2fst command to compile pairs of strings into a transducer. You might need to do some reformatting of the input first.
What are some alternatives?
awesome-hungarian-nlp - A curated list of NLP resources for Hungarian
trankit - Trankit is a Light-Weight Transformer-based Python Toolkit for Multilingual Natural Language Processing
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 .
tldr-transformers - The "tl;dr" on a few notable transformer papers (pre-2022).
Sentiment - An example project using a feed-forward neural network for text sentiment classification trained with 25,000 movie reviews from the IMDB website.
thesaurus - Offline database of synonyms/thesaurus
afinn - AFINN sentiment analysis in Python
Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
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.
Blind-App-Reviews - Scraped reviews of over 25 companies from the Blind App ⚡️
financial-news-dataset - Reuters and Bloomberg