Sentiment classification using principal component analysis based neural network model

The rapid growth of online social media acts as a medium where people contribute their opinion and emotions as text messages. The messages include reviews and opinions on certain topics such as movie, book, product, politics and so on. Opinion mining refers to the application of natural language processing, computational linguistics, and text mining to identify or classify whether the opinion expressed in text message is positive or negative. Back Propagation Neural Networks is supervised machine learning methods that analyze data and recognize the patterns that are used for classification. This work focuses on binary classification to classify the text sentiment into positive and negative reviews. In this study Principal Component Analysis (PCA) is used to extract the principal components, to be used as predictors and back propagation neural network (BPN) have been employed as a classifier. The performance of PCA+ BPN and BPN without PCA has been compared using Receiver Operating Characteristics (ROC) analysis. The classifier is validated using 10-Fold cross validation. The result shows the effectiveness of BPN with PCA used as a feature reduction method for text sentiment classification.