Robust Sentiment Detection on Twitter from Biased and Noisy Data
作者： Junlan Feng , Luciano Barbosa
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摘要: In this paper, we propose an approach to automatically detect sentiments on Twitter messages (tweets) that explores some characteristics of how tweets are written and meta-information the words compose these messages. Moreover, leverage sources noisy labels as our training data. These were provided by a few sentiment detection websites over twitter experiments, show since features able capture more abstract representation tweets, solution is effective than previous ones also robust regarding biased data, which kind data sources.
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acm.org 参考文章(15)
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