Sentiment classification of movie reviews: a powerful method based on ensemble of classifiers and features

Identifying the sentiment of movie reviews is crucial in the film industry, as it can inform movie recommendations and aid in the creation of successful films. However, existing sentiment classification methods still suffer from limitations in two key aspects: representation and classification. To this end, we propose a powerful method that improves sentiment classification in two ways. First, a new feature selection ensemble (FSE) approach is designed to enhance the representation by identifying the most informative subset of movie review features. Second, an improved meta-learning-based dynamic ensemble selection (META-DES) approach is proposed to enhance the sentiment identification process of the movie reviews. The experiments on several real-world datasets demonstrate the effectiveness of both FSE and dynamic ensemble selection in recognizing the sentiment of movie reviews. Moreover, the comparison results between the improved META-DES and existing state-of-the-art of sentiment classification also indicate the competitiveness of our approach for sentiment classification of movie reviews.