An efficient Cucconi based feature extraction with random decision forest classification for improved sentiment analysis

Abstract

Sentiment analysis is a form of opinion mining technique that identifies the polarity of extracted opinions. Nowadays, opinion mining has become an important research area in recent decades to identify the polarity of the statements. Various research works have been carried out on sentiment analysis. However, the existing sentimental analysis techniques, such as time and space complexity, still have considerable limitations. To deal with these issues, this paper proposed the Cucconi Feature Extracted Random Decision Forest Classification (CFDFC) Approach. The main objective of the CFDFC approach is to provide effective sentiment analysis with improved accuracy and reduced time complexity. The proposed CFDFC framework comprises pre-processing, feature extraction, and classification. The pre-processing step eliminates stop words and stem words from user reviews. After the pre-processing step, the feature extraction process is carried out to minimize the dimensionality and time consumption for opinion classification. Cucconi's projective feature extraction process is used in this work to reduce dimensionality. Finally, the classification process is formulated using a random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements. The performance evaluation of the proposed approach is carried out using performance metrics such as accuracy, error rates, recall values, and time and space complexities concerning the number of review statements gathered from the dataset. The results show that the proposed CFDFC model achieves remarkable accuracy, recall, and minimal time complexity compared to existing methods.