DEEP BIDIRECTIONAL RECURRENT NEURAL NETWORKS-BASED SENTIMENTAL ANALYSIS OVER BIG DATA

Abstract

The significant opportunities and
challenges in the research of text mining is
realized in the recent days due to the speedy
increase in the amount of unstructured textual
data with suitable tools for investigating
them. This Deep Bidirectional Recurrent
Neural Networks-based sentimental analysis
Approach is determined to be sentiment
polarity, since it is capable of preparing a
dataset with sentiment for the objective of
training and testing that is potential in
extracting unbiased opinions. In this paper,
Deep Bidirectional Recurrent Neural
Networks-based Sentiment Analysis
(DBRNN-SA) Scheme was proposed over
Big data for preventing the challenges and
investing the vital opportunities in the
process of text mining. This proposed
DBRNN-SA Scheme in particular is
contributed to establish a framework that
facilitates opinion mining using sentimental
analysis for the case of students’ university
choice feedback. This proposed DBRNN-SA
Scheme is compared with the existing
frameworks in order to determine a reliable
deep neural network that aids as a suitable
classification entity in the process of
sentimental analysis.