/**
* Take webpage content and do the following: 1)identify where soft segments are 2)compute score
* based on distribution of terms and similarity 3)move segments around
*/
private void groupSegments(Document doc) {
Cluster cluster = new Cluster();
boolean isSameCluster;
int textSize = doc.body().text().split(" ").length;
int count = 0;
for (int i = 0; i < webPageSegments.size(); i++) {
isSameCluster = cluster.isSameCluster(webPageSegments.get(i));
if (!isSameCluster) {
if (cluster.segments.size() != 0) {
count += cluster.getSegmentText().split(" ").length;
double position = count / ((double) textSize);
if (cluster.getText().trim().length() > 100) {
addNewWebPageSection(cluster, position);
}
}
cluster = new Cluster();
}
cluster.addSegment(webPageSegments.get(i));
}
// last cluster
if (cluster != null) {
count += cluster.getSegmentText().split(" ").length;
double position = count / ((double) textSize);
if (cluster.getText().trim().length() > 100) {
addNewWebPageSection(cluster, position);
}
}
}
private void addNewWebPageSection(Cluster cluster, double pageLocation) {
WebPageSection webPageSection = new WebPageSection();
webPageSection.addCluster(cluster);
// Need to add segment name
webPageSection.setSectionSize(cluster.getSegmentText().split(" ").length);
CharacterBasedDistribution distribution =
new CharacterBasedDistribution(cluster.getSegmentText());
webPageSection.setDistrbution(distribution.distribution);
webPageSection.setPageLocation(pageLocation);
clusters.add(webPageSection);
}
