Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/10036
Author(s): | Batista, F. Ribeiro, R. |
Date: | 2013 |
Title: | Sentiment analysis and topic classification based on binary maximum entropy classifiers |
Volume: | 50 |
Pages: | 77-84 |
ISSN: | 1135-5948 |
Keywords: | Sentiment analysis Topic detection Social media Logistic regression Maximum entropy |
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. |
Peerreviewed: | Sim |
Access type: | Embargoed Access |
Appears in Collections: | CTI-RI - Artigos em revistas científicas internacionais com arbitragem científica |
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File | Description | Size | Format | |
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publisher_version_PLN_50_09.pdf Restricted Access | 745,46 kB | Adobe PDF | View/Open Request a copy |
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