Ensemble feature analysis classifier for sentiment analysis using convolutional neural networks

Text mining is the worldwide fast growing domain in research. Sentiment analysis is the one of the sub domain in the text mining to extract the sentiment from the various texts available in the internet and from other sources. Various existing systems are implemented to get the sentiment analysis with the migration of natural language processing algorithms (NLP) and artificial intelligence algorithms. Various issues identified in the text mining with sentiment analysis are solved very rarely. According to the previous research, deep-learning and artificial intelligencebased TSA prediction method that comprises of a stacked auto encoder (SAE) model that is used to learn generic linguistic and text semantic features But the system not reached up to the mark. In this paper, Ensemble Feature Analysis Classifier to incorporate the new domain dimension within the rating and text based sentiment analyzer. Implementation of this proposed prototype validates our claim and highlights our efficiency in supporting multiple dimensions during sentiment analysis.