Cucconi Feature Extracted Random Decision Forest Classification for Efficient Sentiment Analysis

Abstract

Sentiment is the method of conveying the opinion by the customer for particular product e-commerce website. Sentiment analysis is otherwise termed as opinion mining that identifies the polarity of extracted opinions. Nowadays, opinion mining concept received large interest by many researchers to identify the polarity of the statements. A number of scholars have conducted their research on efficient sentiment analysis with better accuracy. But the space complexity and time complexity performance did not reduced using existing classification techniques. To deal with these issues, CFDFC (Cucconi Feature Extracted Random Decision Forest Classification) Approach is presented. The primary aim of CFDFC Approach is to perform effective sentiment analysis with improved accuracy and reduced time complexity. The number of user review statement is collected from the input dataset. The proposed CFDFC Technique comprises of pre-processes, feature extractions, and classifications. Pre-processes eliminate stop words and stem words from user reviews. After pre-processing, the features (i.e. keywords) extraction process is performed to minimize the dimensionality and time consumption for opinion classification. This work uses cucconi projective feature extractions in CFDFC to squeeze out opinion words from reviews where cucconi projective feature extraction is generally a dimensionality reduction technique used for identifying the projections in multidimensional data. Finally, the classification process is carried out using random decision forest classifier. The random decision forest classifier uses the ID3 DT (decision tree) as a weak learner to classify the review statements with the extracted features. By using the voting scheme, random decision forest classifications finally classify instances with higher accuracy. This again is useful to boost the performance of sentiment analysis. The comprehensive experimental assessment is carried out using various parameters including accuracy, error rates, recall values , time and space complexities with regard to the number of review statements gathered from the dataset.The statistical results obtained show that the proposed CFDFC Technique achieves remarkable accuracy, recall, and minimal time complexity than the current methods.