CENSORED REGRESSIVE ZIJDENBOS INDEXED CONVOLUTIONAL DEEP BELIEF NEURAL NETWORK LEARNING FOR SENTIMENT CLASSIFICATION
Author(s):
Dr. Shubha.S
Keywords:
Censored Regression-based feature extraction, Convolutional Deep Belief Network, Preprocessing, Opinion mining or Sentiment Analysis, Zijdenbos similarity index based classification.
Abstract
Opinion Mining or Sentiment Analysis is a process of classifying the user’s opinion namely positive, negative or neutral about a topic or a product. Traditional sentiment analysis focuses on extracting opinion polarization at a coarse level. Recently, the conventional machine learning technique is analyzed by regarding the customer opinion about the products automatically from online reviews. But it is ineffective and still not possible for evaluating sentiment analysis through enormous data and online processing requirements. In order to improve the Sentiment Classification, a novel Censored Regressive Zijdenbos Indexed Convolutional Deep Belief Network Learning Classification (CRZICDBNLC) technique is introduced. The Convolutional Deep Belief Neural Network Learning Classification technique includes different layers for analyzing the given reviews with different processes namely preprocessing, feature extraction and classification. First the reviews are collected from the dataset and are given to the input layer of Convolutional Deep Belief Network Learning Classifier. Later the reviews are given to the first hidden layer of deep learning where the preprocessing is carried out by removing the stopwords and stem words. Then the Feature extraction process is performed in the second hidden layer using Censored Regression. Finally, the classification is done at the third hidden layer for finding the user’s opinion using the Zijdenbos similarity Index. In this way, accurate Sentiment Classification is performed with higher accuracy. Experimental assessment is carried out with various parameters such as accuracy, precision, recall, F-measure and computational time with respect to a number of reviews. The quantitatively discussed result verifies that the proposed CRZICDBNLC technique achieves higher accuracy and minimal computation time as compared to the conventional methods.
Article Details
Unique Paper ID: 156237

Publication Volume & Issue: Volume 9, Issue 3

Page(s): 74 - 83
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