Machine learning-based multi-documents sentiment-oriented summarization using linguistic treatment

The proposed method combines several types of features into a unified feature set to design a more accurate classification system (“True”: the extractive reference summary; “False”: otherwise). Thus, to achieve better performance scores, we carried out a performance study of four well-known feature selection techniques and seven of the most famous classifiers to select the most relevant set of features and find an efficient machine learning classifier, respectively. The proposed method is applied to three different datasets and the results show the integration of support vector machine-based classification method and Information Gain (IG) as a feature selection technique can significantly improve the performance and make the method comparable to other existing methods. Furthermore, our method that learns from this unified feature set can obtain better performance than one that learns from a feature subset.