Sentiment Analysis using Improved Novel Convolutional Neural Network (SNCNN)

Abstract

Sentiment Analysis is an important method in which many researchers are working on the automated approach for extraction and analysis of huge volumes of user achieved data, which are accessible on social networking websites. This approach helps in analyzing the direct falls under the domain of SA. SA comprises the vast field of effective classification of user-initiated text under defined polarities. The proposed work includes four major steps for solving these issues: the first step is preprocessing which holds tokenization, stop word removal, stemming, cleaning up of unwanted text information like removing of Ads from Web pages, Text normalization for converting binary format. Secondly, the Feature extraction is based on the Bag words, Word2Vec and TF-ID which is a Term Frequency-Inverse Document Frequency. Thirdly, this feature selection includes the procedure for examining semantic gaps along with source features using teaching models and this involves target task characteristic application for Improved Novel Convolutional Neural Network (INCNN). The Feature Selection accompanies the procedure of Information Gain (IG) and PCC which is a Pearson Correlation Coefficient. Finally, the classification step INCNN gives out sentiment posts and responses for the user-based post aspects which helps in enhancing the system performance. The experimental outcome proposes the INCNN algorithm and provides higher performance rather than the existing approach. The proposed INCNN classifier results in highest accuracy.