Can you recommend open source AI libraries for text analysis?
Sergei Petrov
23 replies
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Dennis Harris@dennis_harris1
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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NLTK, spaCy, Gensim, Scikit learn, TensorFlow.
Textblob, Genism,SpaCy, Polyglot, Scikit-learn,NLTK
@apoorva_goel3 do you have with some of these already a little experience?
tensorflow
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.
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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!
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.
Tensorflow
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.