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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
ISSN: 1135-5948
Publisher version: The definitive version is available at:
Appears in Collections:CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica

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