Optimizing accuracy of sentiment analysis using deep learning based classification technique

Abstract

The sentiment or opinion of a person expressed with words and phrases reflect the thoughts of polarities ranging from positive to neutral as well as negative. The emotions hidden in expressions indicate positivity or negativity in the opinion such as words DISTRESS ejaculates more negativity than the word SAD does. Sentiment analysis is the study of concepts of polarities hidden in natural languages. The use of natural language processing is to elucidate information hidden inside the text referred to as sentiment analysis. Sentiment analysis is widely applied to reviews of social media for a variety of applications covering the domains of business, health and government performance evaluations. This type of evaluation extracts the attitude of an author with respect to the context of the topic that to what extent whether the hidden information is related to joy/sadness, amaze/anger, positive and negative emotions. This paper will introduce the proposed technique with Convolution Neural Network used for text classification. The performance of proposed classifier is validated against the performance of Naïve Bayes, J48, BFTree, OneR, LDA and SVM. Examination of efficacies is done on three manually annotated datasets, one dataset is taken from IMDB movie portal and two datasets are infatuated from Amazon product reviews. The accuracies of these seven machine learning techniques are compared and the proposed technique is noticed more precise in generating the accuracy of 85.2% in precision, 82.9% in F-measure and 85.46% of correctly classified sentiment.