<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-04-13T12:32:48Z</responseDate><request verb="GetRecord" identifier="oai:www.recercat.cat:2117/101956" metadataPrefix="qdc">https://recercat.cat/oai/request</request><GetRecord><record><header><identifier>oai:recercat.cat:2117/101956</identifier><datestamp>2025-07-22T18:11:01Z</datestamp><setSpec>com_2072_1033</setSpec><setSpec>col_2072_452951</setSpec></header><metadata><qdc:qualifieddc xmlns:qdc="http://dspace.org/qualifieddc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://purl.org/dc/elements/1.1/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dc.xsd http://purl.org/dc/terms/ http://dublincore.org/schemas/xmls/qdc/2006/01/06/dcterms.xsd http://dspace.org/qualifieddc/ http://www.ukoln.ac.uk/metadata/dcmi/xmlschema/qualifieddc.xsd">
   <dc:title>Targeted data enrichment for an existing large sparse dataset</dc:title>
   <dc:creator>Xue, Zhouyang</dc:creator>
   <dc:subject>Àrees temàtiques de la UPC::Informàtica</dc:subject>
   <dc:subject>Machine learning</dc:subject>
   <dc:subject>Data mining</dc:subject>
   <dc:subject>Aprenentatge automàtic</dc:subject>
   <dc:subject>Mineria de dades</dc:subject>
   <dcterms:abstract>The current work collected a dataset on the interaction between people to be used in&#xd;
future research on sentiment analysis. Based on messages sent from an individual to&#xd;
others, a crawler is build to able to identify individual with high likelihood of response.&#xd;
Based on a random forest model that analyzes features in message and frequent term&#xd;
count analysising the text body, the crawler was able to detect replyied individuals&#xd;
with 75% of acurracy. This allowed us to build a dense and strong connected social&#xd;
network and thus can works for more detailed analysis social researches.</dcterms:abstract>
   <dcterms:issued>2016-12-15</dcterms:issued>
   <dc:type>Bachelor thesis</dc:type>
   <dc:rights>Open Access</dc:rights>
   <dc:publisher>Universitat Politècnica de Catalunya</dc:publisher>
</qdc:qualifieddc></metadata></record></GetRecord></OAI-PMH>