Can you recommend open source AI libraries for text analysis?
Sergei Petrov
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Qudsia Ali@qudsia_ali
WorkHub
Certainly! Some top open-source AI libraries for text analysis include NLTK, spaCy, Gensim, Scikit-learn, and TensorFlow/PyTorch. Each offers unique capabilities, so choose based on your project needs. Happy analyzing!
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Lancepilot
NLTK, spaCy, Gensim, Scikit learn, TensorFlow.
Tensorflow
Textblob, Genism,SpaCy, Polyglot, Scikit-learn,NLTK
code-ray
@apoorva_goel3 do you have with some of these already a little experience?
Hugging Face's transformers library offers pretrained state-of-the-art language models like BERT, GPT and RoBERTa. So, pretty much as good as it gets. You can use it in TensorFlow and PyTorch DL frameworks, usually depending on what you are working with; both should be quite good for text analysis tasks.
@sergeipetrov I've used BERT, DistilBERT and RoBERTa in Tensorflow for NLP tasks, and was quite happy with it. They're appropriate for your task as well. It's best to experiment with different models, and you can also try the GPT.
TextBlob is a simple library for processing textual data. It provides a consistent API for diving into common natural language processing tasks.
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Absolutely, Sergei! For text analysis, you should definitely check out NLTK for natural language processing tasks, and SpaCy for more advanced text analytics and language understanding.
Certainly! Some popular open-source AI libraries for text analysis include NLTK (Natural Language Toolkit), spaCy, Gensim, and TextBlob. These libraries offer a wide range of functionalities such as tokenization, part-of-speech tagging, sentiment analysis, and more, making them valuable resources for text analysis tasks. Alverna used it.
