Background and Objectives: With the extensive web applications, review sentiment classification has attracted increasing interest among text mining works. Traditional approaches did not indicate multiple relationships connecting words while emphasizing the preprocessing phase and data reduction techniques, making a huge performance difference in classification.
Methods: This study suggests a model as an efficient model for sentiment classification combining preprocessing techniques, sampling methods, feature selection methods, and ensemble supervised classification to increase the classification performance. In the feature selection phase of the proposed model, we applied n-grams, which is a computational method, to optimize the feature selection procedure by extracting features based on the relationships of the words. Then, the best-selected feature through the particle swarm optimization algorithm to optimize the feature selection procedure by iteratively trying to improve feature selection.
Results: In the experimental study, a comprehensive range of comparative experiments conducted to assess the effectiveness of the proposed model using the best in the literature on Twitter datasets. The highest performance of the proposed model obtains 97.33, 92.61, 97.16, and 96.23% in terms of precision, accuracy, recall, and f-measure, respectively.
Conclusion: The proposed model classifies the sentiment of tweets and online reviews through ensemble methods. Besides, two sampling techniques had applied in the preprocessing phase. The results confirmed the superiority of the proposed model over state-of-the-art systems.