Search Results for "rtp-text"
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Showing 13 open source projects for "rtp-text"
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natural
General natural language facilities for node
"Natural" is a general natural language facility for nodejs. It offers a broad range of functionalities for natural language processing. Tokenizing, stemming, classification, phonetics, tf-idf, WordNet, string similarity, and some inflections are currently supported. It’s still in the early stages, so we’re very interested in bug reports, contributions and the like. Note that many algorithms from Rob Ellis’s node-nltools are being merged into this project and will be maintained from here... -
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NVIDIA NeMo
Toolkit for conversational AI
NVIDIA NeMo, part of the NVIDIA AI platform, is a toolkit for building new state-of-the-art conversational AI models. NeMo has separate collections for Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Text-to-Speech (TTS) models. Each collection consists of prebuilt modules that include everything needed to train on your data. Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. ... -
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HanLP
Han Language Processing
...Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis. It comes with pretrained models for numerous languages including Chinese and English. It offers efficient performance, clear structure and customizable features, with plenty more amazing features to look forward to on the roadmap. -
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SentimentAnalysis-Rick&Morty
Rick & Morty Sentiment Analysis - End-of-Degree Project - UNIR
The remarkable progress in the field of Big Data has driven the development of new technologies in natural language processing and data analysis. Text mining is a fascinating application of data analysis that extracts relevant information from related writings in different linguistic contexts. And therefore, in natural language processing, sentiment analysis and classification stands out as a key application supported by text mining. Through the extraction of information from textual data, it becomes possible to identify and comprehend the sentiments and emotions conveyed. ... -
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NLP.js
An NLP library for building bots
...Sentiment Analysis for phrases (with negation support). Named Entity Recognition and management, multi-language support, and acceptance of similar strings, so the introduced text does not need to be exact. Natural Language Processing Classifier, to classify an utterance into intents. NLP Manager, a tool able to manage several languages, the Named Entities for each language, the utterances, and intents for the training of the classifier, and for a given utterance return the entity extraction, the intent classification and the sentiment analysis. -
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yabasta
Yet Another BAsic Scraper and Text Analysis
YA BASTA! is a Python/R application for Lyrics Web Scraper and Text Analysis. Web scraping is developed in Python, text analysis in R as Python subprocesses. YA BASTA! is only tested on windows OS. To run YA BASTA! just type on window command prompt: python.exe yabasta.py -
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fastText
Library for fast text classification and representation
FastText is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices. ext classification is a core problem to many applications, like spam detection, sentiment analysis or smart replies. In this tutorial, we describe how to build a text classifier with the fastText tool. -
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TEXT2DATA
Text Analytics Platform
Bring Text Analytics Platform that uses NLP (Natural Language Processing) and Machine Learning to your work environment. Extract essential information from your text documents and let Artificial Intelligence save your time. Get detailed and agile reports on your unstructured data. -
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First-Algerian-Sentiment-Analyser
Sentiment Analysis System for Vernacular Algerian Language
This project is a free GPL licenced Lexicon-based Sentiment Analysis System for Vernacular Algerian Language, it contain 4 lexicons (L1, L2, L3 and L4) and a data set. It aims to give the polarity and the subjectivity for a given text. -
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VADER
Lexicon and rule-based sentiment analysis tool
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments. -
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Speech Sentiment Analysis
Voice to Text Sentiment Analysis
Voice to text Sentiment analysis converts the audio signal to text to calculate appropriate sentiment polarity of the sentence. The code currently works on one sentence at a time. Sentiment scoring is done on the spot using a speaker. The Speech to text processing system currently being used is the MS Windows speech to text converter. However significant modifications can be made for audio recognition by a refined signal processing system. -
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TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. -
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litersta
Litersta - textual analytics - software
Unstructured text is no match for Litersta - see further details here: https://litersta.com Working with text now becomes effortless when paired with Litersta textual analytics software. Unlike database fields, which are easily queried, text contains unstructured data that must be parsed for key objects that can be transformed in to powerful metrics.
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