Please use this identifier to cite or link to this item:

Title: In search of reputation assessment: experiences with polarity classification in RepLab 2013
Authors: Saias, José
Editors: Forner, Pamela
Navigli, Roberto
Tufis, Dan
Keywords: opinion mining
reputation assessment
Machine Learning
Issue Date: Sep-2013
Citation: José Saias. In search of reputation assessment: Experiences with polarity classification in replab 2013. In Pamela Forner, Roberto Navigli, and Dan Tufis, editors, CLEF 2013 Evaluation Labs and Workshop Online Working Notes - Online Reputation Management (RepLab), Valencia, Spain, September 2013.
Abstract: The diue system uses a supervised Machine Learning approach for the polarity classification subtask of RepLab. We used the Python NLTK for preprocessing, including file parsing, text analysis and feature extraction. Our best solution is a mixed strategy, combining bag-of-words with a limited set of features based on sentiment lexicons and superficial text analysis. This system begins by applying tokenization and lemmatization. Then each tweet content is analyzed and 18 features are obtained, related to presence of polarized term, negation before polarized expression and entity reference. For the first run, the learning and classification were performed with the Decision Tree algorithm, from the NLTK framework. In the second run, we used a pipeline of classifiers. The first classifier applies Naive Bayes in a bag-of-words feature model, with the 1500 most frequent words in the training set. The second classifier used the features from the first run plus another feature with the result from the previous classifier. Our system's best result had 0.54694 Accuracy and 0.31506 in F measure.
ISBN: 978-88-904810-5-5
Type: article
Appears in Collections:INF - Artigos em Livros de Actas/Proceedings

Files in This Item:

File Description SizeFormat
CLEF2013wn-RepLab-Saias2013.pdf147.15 kBAdobe PDFView/Open
FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpaceOrkut
Formato BibTex mendeley Endnote Logotipo do DeGóis 

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


Dspace Dspace
DSpace Software, version 1.6.2 Copyright © 2002-2008 MIT and Hewlett-Packard - Feedback
UEvora B-On Curriculum DeGois