Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/21077
Title: TVPulse: Improvements on detecting TV highlightsin Social Networks using metadata and semanticsimilarity
Author: Vilaça, Afonso
Antunes, Mário
Gomes, Diogo
Issue Date: Nov-2015
Publisher: ATNoG - Aveiro Telecommunications and Networking Group
Abstract: Sharing live experiences in social networks is agrowing trend. That includes posting comments and sentimentsabout TV programs. Automatic detection of messages withcontents related to TV opens new opportunities for the industryof entertainment information.This paper describes a system that detects TV highlights in oneof the most important social networks - Twitter. Combining Twit-ter's messages and information from an Electronic ProgrammingGuide (EPG) enriched with external metadata we built a modelthat matches tweets with TV programs with an accuracy over80{\%}. Our model required the construction of semantic profilesfor the Portuguese language. These semantic profiles are usedto identify the most representative tweets as highlights of a TVprogram. Measuring semantic similarity with those tweets it ispossible to gather other messages within the same context. Thisstrategy improves the recall of the detection. In addition wedeveloped a method to automatically gather other related webresources, namely Youtube videos. TVPulse: Improvements on detecting TV highlights in Social Networks using metadata and semantic similarity.
Peer review: yes
URI: http://hdl.handle.net/10773/21077
Publisher Version: http://atnog.av.it.pt/content/tvpulse-improvements-detecting-tv-highlights-social-networks-using-metadata-and-semantic-sim
Appears in Collections:DETI - Comunicações
IT - Comunicações

Files in This Item:
File Description SizeFormat 
bare_conf.pdf380.06 kBAdobe PDFView/Open


FacebookTwitterLinkedIn
Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.