/*
The following is not free software. You may use it for educational purposes, but you may not redistribute or use it commercially.
(C) All Rights Reserved, Burak Kanber 2013
*/
// Define a couple of global variables so we can easily inspect data points we guessed incorrectly on.
var incorrectNegs = [];
var incorrectPos = [];
// A list of negation terms that we'll use to flag nearby tokens
var negations = new RegExp("^(never|no|nothing|nowhere|noone|none|not|havent|hasnt|hadnt|cant|couldnt|shouldnt|wont|wouldnt|dont|doesnt|didnt|isnt|arent|aint)$");
// Use 85% of our data set for training, the remaining 15% will be used for testing.
var length = negatives.length;
var split = Math.floor(0.85 * length);
// Don't spit out console.log stuff during training and guessing.
Bayes.debug = false;
// Close-proximity negation-marked unigram ("eMSU")
Bayes.tokenizer = function (text) {
// Standard unigram tokenizer; lowercase, strip special characters, split by whitespace
text = Bayes.unigramTokenizer(text);
// Step through our array of tokens
for (var i = 0, len = text.length; i < len; i++) {
// If we find a negation word, add an exclamation point to the word preceding and following it.
if (text[i].match(negations)) {
if (typeof text[i + 1] !== 'undefined') text[i + 1] = "!" + text[i + 1];
if (typeof text[i - 1] !== 'undefined') text[i - 1] = "!" + text[i - 1];
}
}
// Porter Stemmer; this reduces entropy a bit
text = text.map(function (t) { return stemmer(t); });
return text;
};
// Set the storage engine to in-memory; this example has too much data for localStorage.
Bayes.storage = Storage;
// Runs a single training and testing experiment.
function go() {
// Start from scratch.
var correct = 0;
var incorrect = 0;
var skipped = 0;
var trainingBar = document.getElementById("trainingProgressBar");
var trainingVal = document.getElementById("trainingProgressValue");
var trainingPct = 0;
var resultsBar = document.getElementById("testResultsBar");
var resultsVal = document.getElementById("testResultsValue");
var resultsPct = 0.0;
Bayes.storage._data = {};
// Shuffle our arrays. I'm sure some really astute CS genius will find a flaw with this ;)
negatives.sort(function () { return Math.random() - 0.5; });
positives.sort(function () { return Math.random() - 0.5; });
// First we train. Walk through the data until we hit our split/pivot point.
// Unfortunately our progress bar doesn't work because of the browser's JS event loop,
// And retooling to use animation frames is more annoying than it's worth.
for (var i = 0; i < split; i++) {
Bayes.train(negatives[i], 'negative');
Bayes.train(positives[i], 'positive');
if (i % 500 === 0) {
// Next three lines are largely useless.
trainingPct = Math.round(i*100 / split);
trainingVal.innerHTML = trainingPct;
trainingBar.style.width = trainingPct + '%';
// If you want live updates, look at the console.
console.log("Training progress: " + trainingPct + "%");
}
}
// Clean up the progress bar for the final state.
trainingPct = 100;
trainingVal.innerHTML = trainingPct;
trainingBar.style.width = trainingPct + '%';
// Now we guess. Look at the remainder of the data set and test each of those.
for (var i = split; i < length; i++) {
var negResult = Bayes.extractWinner(Bayes.guess(negatives[i]));
var posResult = Bayes.extractWinner(Bayes.guess(positives[i]));
// Probability less than 75%? Skip it. No sense in making guesses that we know are uncertain.
if (negResult.score < 0.75) skipped++;
else if (negResult.label === 'negative') correct++;
else {
incorrect++;
incorrectNegs.push(negatives[i]);
}
// Repeat for the corresponding positive data point.
if (posResult.score < 0.75) skipped++;
else if (posResult.label === 'positive') correct++;
else {
incorrect++;
incorrectPos.push(positives[i]);
}
}
// Show the accuracy for this training/testing run.
resultsPct = Math.round(10000 * correct / (correct + incorrect)) / 100;
resultsBar.style.width = Math.round(resultsPct) + '%';
resultsVal.innerHTML = resultsPct;
return resultsPct;
}
document.getElementById("testButton").addEventListener('click', function() {
var text = document.getElementById("testBox").value;
var result = Bayes.extractWinner(Bayes.guess(text));
document.getElementById("testBox").value = '';
document.getElementById("testResultLabel").innerHTML = result.label;
document.getElementById("testResultProbability").innerHTML = Math.round(100*result.score);
document.getElementById("testResult").style.display = 'block';
});
setTimeout(go, 500);
// We're not using this function in the public example, but this is a simple helper to run 30 tests at a time and record and average their scores.
function run() {
var n = 30;
var i = n;
var scores = [];
var sum = 0;
while (i--) scores.push(go());
scores.forEach(function (score) {
sum += score;
});
console.log(scores);
console.log("Average " + sum / n);
}
<body>
<p>Training progress: <span id="trainingProgressValue">0</span>%</p>
<div class="progress-wrapper">
<div class="progress" id="trainingProgressBar"></div>
</div>
<p>Test results: <span id="testResultsValue">0</span>% accuracy</p>
<div class="progress-wrapper">
<div class="progress" id="testResultsBar"></div>
</div>
<p>Test your own:</p>
<textarea id="testBox" placeholder="eg: The director is a master at manupulating your emotions, making you laugh and cry."></textarea>
<button id="testButton">Guess Sentiment</button>
<p id="testResult" style="display:none;">Guess: <span id="testResultLabel"></span> (<span id="testResultProbability"></span>% probability)</p>
</body>
body, textarea {
font-family: Helvetica, Arial, sans-serif;
width: 300px;
margin: 10px auto;
font-size: 13px;
}
.progress-wrapper {
border: 1px solid #CCC;
height: 15px;
width: 100%;
}
.progress {
background: #DDD;
height: 100%;
width: 0%;
}
textarea, button {
border: 1px solid #CCC;
}
textarea {
padding: 10px;
resize: none;
-moz-box-sizing: border-box;
box-sizing: border-box;
margin-top: 0;
}
button {
background: white;
padding: 5px;
cursor: pointer;
}