Abstract
In this paper, we observe the effects that discourse function attribute to the task of training learned classifiers for sentiment analysis. Experimental results from our study show that training on a corpus of primarily persuasive documents can have a negative effect on the performance of supervised sentiment classification. In addition we demonstrate that through use of the Multinomial Naïve Bayes classifier we can minimise the detrimental effects of discourse function during sentiment analysis.
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Smith, P., Lee, M. (2014). Acknowledging Discourse Function for Sentiment Analysis. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8404. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54903-8_4
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DOI: https://doi.org/10.1007/978-3-642-54903-8_4
Publisher Name: Springer, Berlin, Heidelberg
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