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- Generate Focus Key Phrase From A Page To Word
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The free version of Keyword Tool can generate up to 750+ keywords from Google autocomplete in seconds. The advanced version of Keyword Tool, Keyword Tool Pro, provides on average two times more keywords in comparison to the free version and offers a handful of other useful features. Mar 15, 2018 A keyword, or a focus keyword as some call it, is a word that describes the content on your page or post best. It’s the search term that you want to rank for with a certain page. So when people search for that keyword or phrase in Google or other search engines, they should find that page on.
This article shows you how to detect language, analyze sentiment, extract key phrases, and identify linked entities using the Text Analytics APIs with Ruby.
Tip
For detailed API technical documentation and to see it in action, use the following links. You can also send POST requests from the built-in API test console. No setup is required, simply paste your resource key and JSON documents into the request:
- Latest stable API - v2.1
- Latest preview API - v3.0-Preview.1
Prerequisites
A key and endpoint for a Text Analytics resource. Azure Cognitive Services are represented by Azure resources that you subscribe to. Create a resource for Text Analytics using the Azure portal or Azure CLI on your local machine. You can also:
- Get a trial key valid for seven days for free. After signing up, it will be available on the Azure website.
- View your resource on the Azure portal
Detect language
The Language Detection API detects the language of a text document, using the Detect Language method.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Language detection response
Generate Focus Key Phrase From A Page Examples
A successful response is returned in JSON, as shown in the following example:
Analyze sentiment
The Sentiment Analysis API detects the sentiment of a set of text records, using the Sentiment method. The following example scores two documents, one in English and another in Spanish.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Sentiment analysis response
A successful response is returned in JSON, as shown in the following example:
Generate Focus Key Phrase From A Page Youtube
Extract key phrases
The Key Phrase Extraction API extracts key-phrases from a text document, using the Key Phrases method. The following example extracts key phrases for both English and Spanish documents.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Key phrase extraction response
A successful response is returned in JSON, as shown in the following example:
Entity recognition
The Entities API extracts entities in a text document, using the Entities method. The following example identifies entities for English documents.
- Create a new Ruby project in your favorite IDE.
- Add the code provided below.
- Copy your Text Analytics key and endpoint into the code.
- Run the program.
Entity extraction response
A successful response is returned in JSON, as shown in the following example:
Next steps
See also
Text Analytics overview
Frequently asked questions (FAQ)
Frequently asked questions (FAQ)
Built this package as a toy challenge to do the following:
1 - Compute the most important key-words (a key-word can be between 1-3 words)
2 - Choose the top n words from the previously generated list. Compare these key- words with all the words occurring in all of the transcripts.
3 - Generate a score (rank) for these top n words based on analysed transcripts.
What this package does:
1 - Generates the keywords (from 1-3 words in length) from a document based, based on the RAKE algorithm
2 - Generate vector representations of all key words and words in a test corpus, using Word2Vec.
3 - Ranks key words by comparing key word vectors with paragraph/document vectors from test corpus
4 - Saves ranked keywords to text file (and/or displays on the console)
Generate Focus Key Phrase From A Page Free
Installing dependencies
The code was developed with python 3.5 and requires the following libraries/versions:
gensim2.0.0numpy1.12.1scikit-learn0.18.1wget3.2
These dependencies are specified in requirements.txt, and can be downloaded via the following command:
Usage
Running the keyword_xtract file, will carry out the steps described above (keyword extraction -> compute vector representations -> rank key words)
Models available:
A truncated version of Google's pre-trained Word2Vec model is available as default. GloVe Word2Vec models (https://nlp.stanford.edu/projects/glove/) can also be downloaded by specifying the model required at run time:
Generate Focus Key Phrase From A Page Pdf
glove_6B - Wikipedia 2014 + Gigaword 5 (6B tokens, 400K vocab, uncased, 50d, 100d, 200d, & 300d vectors, 822 MB download)glove_42B - Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors, 1.75 GB download): glove.42B.300d.zipglove_840B - Common Crawl (840B tokens, 2.2M vocab, cased, 300d vectors, 2.03 GB download): glove.840B.300d.zipglove_twitter - Twitter (2B tweets, 27B tokens, 1.2M vocab, uncased, 25d, 50d, 100d, & 200d vectors, 1.42 GB download)
Use the labels above as inputs for the '-m/--model' command line arguments. If the selected model is not present, the model will be downloaded; this may take some time. It is also possible to use custom user-defined Word2Vec models by supplying a path to the model.
NOTE - the default evaluation docs provided for ranking keywords are 3 document pages related to food, which were extracted from Wikipedia. Please provide your own relevant evaluation documents for accurate keyword ranking. Otherwise, keywords can simply be extracted and the ranking scores ignored.
RAKE algorithm + implementation
I modified an existing RAKE implementation to work with Python 3 and different parameters. In this implementation, RAKE does the following:
(i) Generate key word candidates(ii) Computes 'scores' for each candidate. Words are scored according to their frequency and the typical length of a candidate phrase in which they appear.
Originally implemented by: https://github.com/aneesha/RAKEForked from: https://github.com/BelalC/RAKE-tutorial/tree/master
A Python implementation of the Rapid Automatic Keyword Extraction (RAKE) algorithm as described in:Rose, S., Engel, D., Cramer, N., & Cowley, W. (2010). Automatic Keyword Extraction from Individual Documents. In M. W. Berry & J. Kogan (Eds.), Text Mining: Theory and Applications: John Wiley & Sons.
The source code is released under the MIT License.
Word2Vec + Ranking
Generate Focus Key Phrase From A Page To Word
Utilising gensim and pre-trained Word2Vec models, keyword vector representations are computed. Vector representations of evaluation documents are computed by taking the average of the word vectors present in a specified document. The pairwise cosine similarity between each keyword vector and evaluation document vector are computed and averaged, giving a single score which can be utilised as a 'rank' for the keyword.
Generate Focus Key Phrase From A Page Book
Gensim - https://radimrehurek.com/gensim/index.htmlVector represenations of words and phrases - Distributed Representations of Words and Phrases and their Compositionality; Mikolov, Tomas; Sutskever, Ilya; Chen, Kai; Corrado, Greg; Dean, Jeffrey, arXiv:1310.4546