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Dewe","email":"johan.dewe@gavagai.se"},"license":"MIT","homepage":"https://github.com/gavagai-corp/gavagai-node","keywords":["api","nlp","text analysis"],"repository":{"url":"git+https://github.com/gavagai-corp/gavagai-node.git","type":"git"},"description":"A Gavagai API helper library","maintainers":[{"email":"webapp@gavagai.se","name":"gavagai"},{"email":"fredrik.espinoza@gmail.com","name":"fredespi"}],"readme":"# gavagai-node\n\nNode.js helper library for Gavagai API\n\n## Install\n\n```bash\n$ npm install gavagai\n```\n\n## Api key\n\nGet your own api key for free at [Gavagai Developer Portal](https://developer.gavagai.se).\n\n## Use\n\nSee [Gavagai API documentation](https://developer.gavagai.se/docs) for details about available API resources.\n\n### Lexicon\nLookup a word in [Gavagai living lexicon](http://lexicon.gavagai.se).\n\n```javascript\nvar gavagai = require('gavagai');\nvar client = gavagai('GAVAGAI_APIKEY');\n\n// getting all info about 'good'\nclient.lexicon('good', function (err, data) {\n    console.log('lexicon returned:', data);\n});\n```\n\n### Stories\nGiven a set of texts, return summaries as a set of stories.\n\n```javascript\nvar gavagai = require('gavagai');\nvar client = gavagai('GAVAGAI_APIKEY');\n\nvar texts = [\n    'Stayed here for 3 nights at the beginning of a trip of California. Could not say enough good things about the hotel Monaco. Amazing staff, amazing rooms and the location is brilliant! First stay at a Kimpton hotel, but definitely not the last!!!',\n    'I did a lot of research looking for a hotel suite for our family vacation in San Francisco. The Hotel Monaco was a perfect choice. What friendly and delightful staff. I will miss the Grand Cafe, but I will make sure to come back to see their new offerings.',\n    'My partner and I spent four nights here over New Years and loved it. Super staff; lovely, quiet room; excellent location within easy walking to much of Downtown and an overall experience that was perfect.'\n];\n\nclient.stories(texts, function(err, data) {\n    if (err) {\n        console.error('error:', err);\n    }\n    console.log('stories returned:', data);\n});\n```\n\n\n### Topics\nGiven a set of texts, return significant topics and keywords.\n\n```javascript\nclient.topics(texts, function(err, data) {\n    if (err) {\n       console.error('error:', err);\n    }\n    console.log('stories returned:', data);\n});\n```\n\n### Keywords\nExtract salient concepts from a collection of texts. Order by number of occurrences.\n\n```javascript\nclient.keywords(texts, function(err, data) {\n    if (err) {\n       console.error('error:', err);\n    }\n    console.log('keywords returned:', data);\n});\n```\n\n### Tonality\nFor each text, return their tonality based on lexical analysis in multiple dimensions.\nThis method accepts either a single string, or an array of texts as input.\n\n```javascript\nclient.tonality(\"it’s beautiful and makes me want to cry\", function(err, data) {\n    if (err) {\n       console.error('error:', err);\n    }\n    console.log('stories returned:', data);\n});\n```\n\n### Options\nAll methods accepts options as a second paramter. Options are corresponding to API parameters.\nE.g. if I have a set of french texts that I want to analyze:\n\n```javascript\nclient.topics(texts, { language: 'fr' }, function(err, data) {\n    if (err) {\n       console.error('error:', err);\n    }\n    console.log('stories returned:', data);\n});\n```\n\n### Document IDs\nIn some situations documents have a specific identifiers. In that case, you can use an array of\nobjects that have properties `id` and `body`.\n\n```javascript\nvar texts = [\n    {\n      id: '1',\n      body: 'Stayed here for 3 nights at the beginning of a trip of California. Could not say enough good things about the hotel Monaco. Amazing staff, amazing rooms and the location is brilliant! First stay at a Kimpton hotel, but definitely not the last!!!',\n    },\n    {\n      id: '2',\n      body: 'I did a lot of research looking for a hotel suite for our family vacation in San Francisco. The Hotel Monaco was a perfect choice. What friendly and delightful staff. I will miss the Grand Cafe, but I will make sure to come back to see their new offerings.',\n    },\n    {\n      id: '3',\n      body: 'My partner and I spent four nights here over New Years and loved it. Super staff; lovely, quiet room; excellent location within easy walking to much of Downtown and an overall experience that was perfect.'\n    }\n];\n```\n\n### Promises\nOne method for for handling the common problem with deeply nested callbacks, is to use promises.\nGavagai-node uses the [Q library](https://github.com/kriskowal/q) to provide an alternative to callbacks.\n\nSee example in [promises.js](examples/promises.js) for how to make an api call with promises.\n\n## Running tests\nStart by install [grunt-cli](https://www.npmjs.com/package/grunt-cli):\n\n```bash\n$ npm install -g grunt-cli\n```\n\nExecute specs:\n\n```bash\n$ grunt\n```\n\nExecute smoke tests against live API:.\n\n```bash\n$ grunt smoke\n```\n\n\n\n\n\n\n","readmeFilename":"README.md","users":{"gavagai":true}}