Sentiment polarity classification using conjure of genetic algorithm and differential evolution methods for optimized feature selection

Objectives: Sentiment Analysis (SA) has a big role in Big data applications regarding consum-er attitude detection, brand/product positioning, customer relationship management and market research. SA is a natural language processing method to track the public mood on a specific product. SA builds a system to collect/examine opinions on a product in comments, blog posts, re-views or tweets. Machine learning applicable to Sentiment Analysis belongs to supervised classifi-cation in general. Methods: Two sets of documents, training and test set are required in machine learning based clas-sification: Training set is used by classifiers to learn documents differentiating character-istics; it is thus called supervised learning. Results: Test sets validate the classifier’s performance. Se-mantic orientation approach to SA is unsupervised learning because it requires no prior training for mining data. It measures how far a word is either positive or negative. This paper uses a hybrid GA-DE optimization technique for sentiment classification to classify features from movie reviews and medical data. Conclusion: Our research has enhanced the variables on learning rate as well as momentum values which are optimized by genetic approach that in turn improve the accuracy of classification procedure. © 2020 Bentham Science Publishers.