Skip navigation
Logo
User training | Reference and search service

Library catalog

Retrievo
EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/10036
acessibilidade
Title: Sentiment analysis and topic classification based on binary maximum entropy classifiers
Authors: Batista, F.
Ribeiro, R.
Keywords: Sentiment analysis
Topic detection
Social media
Logistic regression
Maximum entropy
Issue Date: 2013
Publisher: Sociedad Española para el Procesamiento del Lenguaje Natural
Abstract: This paper presents a strategy based on binary maximum entropy classifiers for automatic sentiment analysis and topic classification over Spanish Twitter data. The developed system achieved the best results for topic classification, and the second place for sentiment analysis in a joint evaluation effort — the TASS challenge. Different configurations have been explored for both tasks, leading to the use of a cascade of binary classifiers for sentiment analysis and a one-vs-all strategy for topic classification, where the most probable topics for each tweet were selected.
Peer reviewed: Sim
URI: https://ciencia.iscte-iul.pt/public/pub/id/13632
http://hdl.handle.net/10071/10036
ISSN: 1135-5948
Publisher version: The definitive version is available at: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/4662
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
publisher_version_PLN_50_09.pdf745.46 kBAdobe PDFView/Open    Request a copy


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

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