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|Title: ||In search of reputation assessment: experiences with polarity classification in RepLab 2013|
|Authors: ||Saias, José|
|Editors: ||Forner, Pamela|
|Keywords: ||opinion mining|
|Issue Date: ||Sep-2013|
|Citation: ||José Saias.
In search of reputation assessment: Experiences with polarity classification in replab
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.|
|Appears in Collections:||INF - Artigos em Livros de Actas/Proceedings|
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