/
index.js
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/
index.js
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/**
* Language sentiment analysis and neural networks... for trolls.
*
* @package troll
* @author Andrew Sliwinski <andrew@diy.org>
*/
/**
* Dependencies
*/
var _ = require('lodash'),
async = require('async'),
brain = require('brain'),
sentiment = require('sentiment'),
redis = require('redis');
var Adapter = require('./adapter'),
Trainer = require('./trainer.json');
/**
* Simple numeric range mapping function.
*
* @param {Number} Input value
* @param {Array} Source range (e.g. [-5,5])
* @param {Array} Destination range (e.g. [0,1])
*
* @return {Number}
*/
function convertToRange(value, srcRange, dstRange){
// value is outside source range return
if (value < srcRange[0] || value > srcRange[1]){
return NaN;
}
var srcMax = srcRange[1] - srcRange[0],
dstMax = dstRange[1] - dstRange[0],
adjValue = value - srcRange[0];
return (adjValue * dstMax / srcMax) + dstRange[0];
}
/**
* Converts redis list into an array of numbers between 0 and 1.
*
* @param {Array} Input array
*
* @return {Array}
*/
function convertList (list, callback) {
// Trim list to the latest 12 values
var set = list.slice(0, 10);
for (var i = set.length; i < 10; i++) {
set.push(0);
}
// Map AFINN data
async.map(set, function (item, callback) {
callback(null, convertToRange(Number(item), [-10,10], [0,1]));
}, callback);
}
/**
* Constructor
*/
function Troll () {
var self = this;
_.defaults(process.env, {
TROLL_HOST: 'localhost',
TROLL_PORT: 6379
});
// Redis connection
var client = redis.createClient(
Number(process.env.TROLL_PORT),
process.env.TROLL_HOST
);
if (typeof process.env.TROLL_PASS !== 'undefined') {
client.auth(process.env.TROLL_PASS, function (err) {
if (err) process.stderr.write(err);
});
}
// Setup
self.adapter = new Adapter(client);
self.net = new brain.NeuralNetwork();
};
/**
* Performs sentiment analysis on a given input.
*
* @param {String} Input text
* @param {String, Optional} Unique user identifier for tracking
*
* @return {Number}
*/
Troll.prototype.analyze = function (input, user, callback) {
var self = this;
// Process args
if (typeof callback === 'undefined') {
callback = user;
user = null;
}
// Analyze input
sentiment(input, function (err, result) {
if (err) return callback(err);
if (user === null) {
callback(null, result.score);
} else {
self.adapter.push(user, result.score, callback);
}
});
};
/**
* Checks the status of a specified user.
*
* @param {String} Unique user identifier
*
* @return {Object}
* - total {Number} Total number of analyses performed
* - sum {Number} Sum of all ratings
* - troll {Number} Probability of this user being a troll
*/
Troll.prototype.classify = function (user, callback) {
var self = this;
// Fetch from datastore
async.auto({
total: function (callback) {
self.adapter.length(user, callback);
},
sum: function (callback) {
self.adapter.sum(user, callback);
},
troll: ['total', 'sum', function (callback, obj) {
self.adapter.all(user, function (err, all) {
if (err) {
callback(err);
} else {
convertList(all, function (err, result) {
callback(null, self.net.run(result)[0]);
});
}
});
}]
}, callback);
};
/**
* Trains the troll detection network. :-)
*
* @param {String} Training data (optional)
*
* @return {Object}
* - error {Number} Training error
* - iterations {Number} Training iterations
*/
Troll.prototype.train = function (data, callback) {
var self = this;
// Process arguments
if (typeof callback === 'undefined') {
callback = data;
data = Trainer;
}
// Train
callback(null, self.net.train(data));
};
/**
* Export
*/
module.exports = new Troll();