sentiment/feeling.</p>
<p>In order to produce this system, a Text Classification technique
has to be adapted to a given application domain. In this demo, the
<a href='http://nlp.cs.swarthmore.edu/semeval/'>SemEval-2007 dataset</a>
is of use for training the classifier, and the learnt model is then
applied to processing similar world news headlines from
The Washington Post:</p>
<?php
// Prepare classifier
$classifier = new MultinomialNaiveBayes();
$classifier->setDatabase("semeval07");
// Prepare data
$feeder = new FeedRSS();
$aFeeds = $feeder->getFood("http://feeds.washingtonpost.com/rss/world");
foreach ($aFeeds as $feed) {
$lab = $classifier->classify($feed["title"]);
if ($lab == "NEG") {
echo "<p class='neg'>";
} elseif ($lab == "NEU") {
echo "<p class='neu'>";
} else {
echo "<p class='pos'>";
}
echo "• " . $feed["title"] . " - <a href='" . $feed["link"] . "'>Read more</a></p>";
}
?>
<?php
putFooter();
