Sentiment Analysis Using Treebank Filtered Preprocess with Relevant Vector Boost Classifier

Abstract

The procedure of identifying and classifying opinions in a piece of text to find out whether customer reviews towards a particular product or service are positive, negative, or neutral is termed as sentiment analysis. Stock market prediction is one of the most attractive topics in academic and real-life business. Many data mining techniques about sentiment analysis are suffering from the inaccuracy of prediction. The low classification accuracy has a direct effect on the reliability of stock market indicators. Treebank filtering Data Preprocessing based Ochiai-Barkman Relevance Vector Linear Programming Boost Classification (TFDP-ORVLPBC) technique is used for stock market prediction using sentimental analysis with higher prediction accuracy and lesser classification time for enhancing accuracy of stock market based on product review. Initially, the customer reviews and feedback on services or products are collected from the large database. After that, the collected customer reviews are preprocessed by performing the process such as tokenization, stemming, filtering. In order to achieve sentimental analysis through classifying customer reviews as positive and negative, Ochiai-Barkman Relevance Vector Linear Programming Boost Classification algorithm is used. The Linear Programming Boost Classification algorithm constructs with an empty set of weak classifiers as the Ochiai-Barkman Relevance Vector machine. The customer reviews are classified based on the Ochiai-Barkman similarity coefficient. The ensemble technique combines the weak classification results into strong by minimizing the error. In this way, the classification performance gets improved and the prediction of the stock market is carried out in a more accurate manner. Experimental evaluation is carried out on factors such as prediction accuracy, sensitivity, specificity, and prediction time versus amount of customer reviews.