Background:
Sentiment analysis of big data such as Twitter primarily aids the organizations with
the potential of surveying public opinions or emotions for the products and events associated with them.
Objective:
In this paper, we propose the application of a deep learning architecture namely the Convolution
Neural Network. The proposed model is implemented on benchmark Twitter corpus (SemEval 2016 and
SemEval 2017) and empirically analyzed with other baseline supervised soft computing techniques. The
pragmatics of the work includes modelling the behavior of trained Convolution Neural Network on wellknown
Twitter datasets for sentiment classification. The performance efficacy of the proposed model has
been compared and contrasted with the existing soft computing techniques like Naïve Bayesian, Support
Vector Machines, k-Nearest Neighbor, Multilayer Perceptron and Decision Tree using precision, accuracy,
recall, and F-measure as key performance indicators.
Methods:
Majority of the studies emphasize on the utilization of feature mining using lexical or syntactic
feature extraction that are often unequivocally articulated through words, emoticons and exclamation marks.
Subsequently, CNN, a deep learning based soft computing technique is used to improve the sentiment classifier’s
performance.
Results:
The empirical analysis validates that the proposed implementation of the CNN model outperforms
the baseline supervised learning algorithms with an accuracy of around 87% to 88%.
Conclusion:
Statistical analysis validates that the proposed CNN model outperforms the existing techniques
and thus can enhance the performance of sentiment classification viability and coherency.
Call for Special Issue Papers: Soft Computing Models for Big Data and Internet of Things