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However, for general use it is recommended that all options are left to their defaults.\n\n### 'encoding'\n\n**String - valid options: 'binary' (default), 'frequency', or 'percent'**\n\nN.B - You probably don't want to change this, ever.\n\nControls how the lexical value is calculated.\n\nBinary is simply the addition of lexical weights, i.e. word1 + word2 + word3.\n\nFrequency encoding takes the overall wordcount and word frequency into account, i.e. (word frequency / word count) * weight.\n\nPercent returns the percentage of token matches in each category as a decimal, i.e. 0.48 - 48%.\n\n### 'locale'\n**String - valid options: 'US' (default), 'GB'**\nThe lexicon data is in American English (US), if the string(s) you want to analyse are in British English set the locale option to 'GB'.\n\n### 'logs'\n**Number - valid options: 0, 1, 2, 3 (default)**\nUsed to control console.log, console.warn, and console.error outputs.\n* 0 = suppress all logs\n* 1 = print errors only\n* 2 = print errors and warnings\n* 3 = print all console logs\n\n### 'max' and 'min'\n\n**Float**\n\nExclude words that have weights above the max threshold or below the min threshold.\n\nBy default these are set to infinity, ensuring that no words from the lexicon are excluded.\n\nLexical weights run from a maximum of 0.91 to a minimum of -0.98.\n\n### 'nGrams'\n\n**Array - valid options: [ number, number, ...]**\n\nn-Grams are contiguous pieces of text, bi-grams being chunks of 2, tri-grams being chunks of 3, etc.\n\nUse the nGrams option to include n-gram chunks. For example if you want to include both bi-grams and tri-grams, use like so:\n\n```javascript\n{\n  nGrams: [2, 3]\n}\n```\n\nIf you only want to include tri-grams:\n\n```javascript\n{\n  nGrams: [3]\n}\n```\n\nTo disable n-gram inclusion, use the following:\n\n```javascript\n{\n  nGrams: [0]\n}\n```\n\nIf the number of words in the string is less than the ngram number provided, the option will simply be ignored.\n\nFor accuracy it is recommended that n-grams are included, however including n-grams for very long strings can affect performance.\n\n### 'noInt'\n\n**Boolean - valid options: true or false (default)**\n\nThe lexica contain intercept values, set noInt to true to ignore these values.\n\nUnless you have a specific need to ignore the intercepts, it is recommended you leave this set to false.\n\n### 'output'\n\n**String - valid options: 'lex' (default), 'matches', or 'full'**\n\n'lex' returns the lexical values for affect and intensity. See \"default output example\" above.\n\n'matches' returns an array of matched words along with the number of times each word appears, its weight, and its final lexical value. See the output section below for an example.\n\n'full' returns an object containing the lexical value and the matches array.\n\n### 'places'\n\n**Number**\n\nNumber of decimal places to limit outputted values to.\n\nThe default is 9.\n\n### 'sortBy'\n\n**String - valid options: 'freq' (default), 'lex', 'weight'**\n\nIf 'output' = 'matches', this option can be used to control how the outputted array is sorted.\n\n'lex' sorts by final lexical value, i.e. (word frequency * word count) / word weight.\n\n'weight' sorts the array by the matched words initial weight.\n\n'freq' (default) sorts by word frequency, i.e. the most used words appear first.\n\n### 'wcGrams'\n\n**Boolean - valid options: true, or false (default)**\n\nWhen set to true, the output from the nGrams option will be added to the word count.\n\nFor accuracy it is recommended that this is set to false.\n\n---\n\n## {output: 'matches'} Output Example\n\n```javascript\n{\n  AFFECT:\n    matches:\n    [\n      [ 'magnificent', 1, -192.0206116, -1.3914537072463768 ],\n      [ 'capital', 1, -133.9311307, -0.9705154398550726 ],\n      [ 'note', 3, -34.83417005, -0.7572645663043478 ],\n      [ 'america', 2, -49.21227355, -0.7132213557971014 ],\n      [ 'republic', 1, -75.5720402, -0.5476234797101449 ]\n    ],\n    info:\n    {\n      total_matches: 100,\n      total_token: 200,\n      total_unique_matches: 64,\n      percent_matches: 50\n    }\n  INTENSITY:\n    [\n      ....\n    ],\n  ...\n};\n```\n\n---\n\n## Acknowledgements\n\n### References\nBased on [Preotiuc-Pietro, D., Schwartz, H.A., Park, G., Eichstaedt, J., Kern, M., Ungar, L., Shulman, E.P. (2016). Modelling Valence and Arousal in Facebook Posts. Proceedings of the Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA), NAACL.](http://wwbp.org/papers/va16wassa.pdf)\n\n### Lexicon\nUsing the affect/intensity lexicon data from [WWBP](http://www.wwbp.org/lexica.html) under the [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported](http://creativecommons.org/licenses/by-nc-sa/3.0/) license.\n\n## License\n(C) 2017-18 [P. Hughes](https://www.phugh.es). All rights reserved.\n\nShared under the [Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported](http://creativecommons.org/licenses/by-nc-sa/3.0/) license.","maintainers":[{"name":"phughes","email":"peter@phugh.es"}],"time":{"modified":"2022-06-13T02:28:58.996Z","created":"2017-04-25T22:48:30.167Z","0.0.1":"2017-04-25T22:48:30.167Z","0.1.0":"2017-04-26T13:14:27.476Z","0.1.1":"2017-04-27T18:23:25.472Z","0.1.2":"2017-04-29T18:29:11.451Z","0.1.3":"2017-05-06T10:50:37.710Z","0.1.4":"2017-05-22T14:36:57.737Z","0.2.0":"2017-06-11T16:00:27.284Z","0.2.1":"2017-06-12T20:24:33.667Z","0.3.0":"2017-06-13T11:26:30.470Z","0.3.1":"2017-08-10T14:27:20.358Z","0.3.2":"2017-08-14T12:19:55.525Z","0.4.0":"2017-08-14T13:22:24.512Z","1.0.0-rc.1":"2017-08-24T15:27:57.730Z","1.0.0-rc.2":"2017-08-27T13:42:18.572Z","1.0.0":"2017-08-27T14:20:45.736Z","1.1.0":"2017-08-30T15:58:04.845Z","1.1.1":"2017-09-24T15:31:52.200Z","2.0.0":"2017-12-29T16:21:17.533Z","2.0.1":"2018-03-18T15:48:36.242Z","2.1.0":"2018-03-19T17:44:53.648Z","2.1.1":"2018-03-22T12:33:47.980Z","3.0.0":"2018-04-23T21:12:15.721Z","3.0.1":"2018-04-25T09:24:40.032Z","3.1.0":"2018-07-22T14:54:59.616Z"},"homepage":"https://github.com/phugh/affectimo","keywords":["sentiment","emotion","mood","psychology","prediction","affect","intensity","analysis","valence","feelings","NLP"],"repository":{"type":"git","url":"git+https://github.com/phugh/affectimo.git"},"author":{"name":"P. 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