flashtext - Extract Keywords from sentence or Replace keywords in sentences.

•    Python

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This module can be used to replace keywords in sentences or extract keywords from sentences. It is based on the FlashText algorithm. Documentation can be found at FlashText Read the Docs.

search-in-text keyword-extraction nlp word2vec data-extraction

rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.

•    Python

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RAKE short for Rapid Automatic Keyword Extraction algorithm, is a domain independent keyword extraction algorithm which tries to determine key phrases in a body of text by analyzing the frequency of word appearance and its co-occurance with other words in the text. If you see a stopwords error, it means that you do not have the corpus stopwords downloaded from NLTK. You can download it using command below.

nltk algorithm text-mining keyword-extraction

retext-keywords - plugin to extract keywords and key-phrases

•    Javascript

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retext plugin to extract keywords and key-phrases. This package is ESM only: Node 12+ is needed to use it and it must be imported instead of required.

tensorflow natural-language keyword retext keyword-extraction term retext-plugin unified plugin phrase terminology extraction

hungarian-text-mining-workshop - Materials for the Text Mining workshop held in the HuNLP meetup, June 2017

•    Jupyter

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During the course we will use little bit of Pandas (10 minute intro) and scikit-learn to build simple machine learning models.

nlp natural-language-processing hungarian spacy spacy-models meetup textacy information-extraction machine-learning classification sentiment-analysis keyword-extraction workshop text-mining-workshop tutorial scikit-learn text-mining

Crackr - Keyword Extraction system using Brown Clustering - (This version is trained to extract keywords from job listings)

•    Python

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Keyword extraction is an extremely interesting topic in Information Retrieval- keywords are widely acknowledged to be extremely important in the field of text retrieval, and particularly while developing large scale modern search engines that limit the size of the inverted index used by the system. In this project we propose to build a system using modern NLP techniques such as Part of Speech Tagging, Brown Clustering and Rapid Automatic Keywords Extraction (RAKE) to use a small initial seed of keywords to generate more candidate keywords in a semi-supervised manner and expose the system as a JSON based web service.

keyword-extraction brown-clustering

abbreviation-extraction - Python3 implementation of the Schwartz-Hearst algorithm for extracting abbreviation-definition pairs

•    Python

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This is a Python3 implementation of the Schwartz-Hearst algorithm for identifying abbreviations and their corresponding definitions in free text[1]. I have taken the liberty of taking Vincent's code, simplifying it a little, refactoring it for Python 3, and adding some tests.

python3 nlp keyword-extraction abbreviations information-extraction

text-summarization-and-visualization-using-watson-studio - Can we quickly summarize & visualize text to get the details about the unstructured data? Yes we can! Please review this code pattern for all the steps involved to quickly summarize & visualize the data

•    Jupyter

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We will demonstrate a methodology to summarize & visualize text using Watson Studio. Text summarization is the process of creating a short and coherent version of a longer document. There are two methods to summarize the text, extractive & abstractive summarization. We will focus on extractive summarization which involves the selection of phrases and sentences from the source document to make up the new summary. Techniques involve ranking the relevance of phrases in order to choose only those most relevant to the meaning of the source. Some of the advantages of text summarization are below. We will also demonstrate different methods to visualize the data which can aid in providing quick peek of the data. Summaries reduce reading time. When researching documents, summaries make the selection process easier.Text summarization improves the effectiveness of indexing.Text summarization algorithms are less biased than human summarizers. Personalized summaries are useful in question-answering systems as they provide personalized information.Using automatic or semi-automatic summarization systems enables commercial abstract services to increase the number of texts they are able to process.

text-mining text-classification topic-modeling text-processing visualization summarization keyword-extraction text-analysis data-science data-visualization data-mining

TextRank - Implementation of TextRank with the option of using pre-trained Word2Vec embeddings as the similarity metric

•    Python

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Implementation of TextRank with the option of using cosine similarity of word vectors from pre-trained Word2Vec embeddings as the similarity metric.

word2vec pagerank pagerank-algorithm textrank similarity keywords keyword cosine-similarity keyword-extraction textrank-algorithm cosine-distance cosine keyword-extractor cosine-similarity-scores textrank-python keywords-extraction pagerank-python cosinesimilarity

OpnEco - OpnEco is a Python3 project developed to aid content writers throughout the content writing process

•    HTML

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OpnEco is a Python3 project developed just for that. To be a constant companion throughout your content writing process. By content writers, for content writers.

django analytics python3 keyword-extraction emotion-detection content-writing