spaCy
Amazon Comprehend
Google Cloud Natural Language API
FuzzyWuzzy
Microsoft Bing Spell Check API
Medallia
NLTK
OpenNLP
Apache OpenNLP
Medallia
Amazon Comprehend
Google Cloud Natural Language API
Rosette
FuzzyWuzzy
AYLIEN
Lexalytics Semantria
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Based on our record, spaCy seems to be a lot more popular than Apache OpenNLP. While we know about 59 links to spaCy, we've tracked only 3 mentions of Apache OpenNLP. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Is this just using LLM to be cool? How does pure LLM with simple "In the scale between 0-10"" stack up against traditional, battle-tested sentiment analysis tools? Gemini suggests NLTK and spaCy https://www.nltk.org/ https://spacy.io/. - Source: Hacker News / 9 months ago
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):. - Source: dev.to / about 1 year ago
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging. Source: over 1 year ago
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy. - Source: dev.to / over 1 year ago
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post. The steps... - Source: Hacker News / over 1 year ago
If your looking at spacy have a look at Apache OpenNLP and Core NLP. Source: over 2 years ago
If you want to deeper, you may want to check out something like spaCy (.NET) (or OpenNLP or LUIS or ... Many more ...) to do the heavy lifting of semantic normalization, and then go from there. Source: over 3 years ago
If you need NER, there’s no need to implement it yourself. There are several popular libraries that can do this for you nowadays. Five of these libraries, Stanford CoreNLP, NLTK, OpenNLP, SpaCy, and GATE, were already mentioned in the title. - Source: dev.to / over 3 years ago
Amazon Comprehend - Discover insights and relationships in text
Medallia - Medallia enables companies to capture customer feedback, understand it in real-time, and take action to improve the customer experience (CX).
Google Cloud Natural Language API - Natural language API using Google machine learning
FuzzyWuzzy - FuzzyWuzzy is a Fuzzy String Matching in Python that uses Levenshtein Distance to calculate the differences between sequences.
Microsoft Bing Spell Check API - Enhance your apps with the Bing Spell Check API from Microsoft Azure. The spell check API corrects spelling mistakes as users are typing.
Rosette - Rosette text analytics is a robust toolkit for processing language, documents, and names.
