2015/11/07 by Sylvester Olubolu Orimaye, Orimaye, Sylvester Olubolu, Saadat M. Alhashmi +5
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1511.02385
openalex publication_date 2015/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose an effective technique to solving review-level sentiment classification problem by using sentence-level polarity correction. Our polarity correction technique takes into account the consistency of the polarities (positive and negative) of sentences within each product review before performing the actual machine learning task. While sentences with inconsistent polarities are removed, sentences with consistent polarities are used to learn state-of-the-art classifiers. The technique achieved better results on different types of products reviews and outperforms baseline models without the correction technique. Experimental results show an average of 82% F-measure on four different product review domains.