Ensemble Learning for Sentiment Classification
Lecture notes in computer sciencePublished 1 January 2013
Ying Su, Yong Zhang, Donghong Ji, Yibing Wang, Hongmiao Wu
Citations42
SJR quartileQ2
SJR score0.35
SNIP0.55
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TL;DR
Results show that stacking has been proven to be consistently effective over all domains, working better than majority voting, and that using the opinion summary can improve the performance further.
Abstract
This paper presents an ensemble learning method for sentiment classification of reviews. The diversity among the machine learning algorithms for sentiment classification with different settings, which includes different features, different weight measures and the modeling of negation, is investigated in three domains, which gives a space for improving the performance. Then the ensemble learning framework, stacking generalization is introduced based on different algorithms with different settings, and compared with the majority voting. According to the characteristic of reviews, the opinion summary of review is proposed in this paper, which is composed of the first two and last two sentences of review. Results show that stacking has been proven to be consistently effective over all domains, working better than majority voting, and that using the opinion summary can improve the performance further.
Keywords
Computer Science
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