Sentiment Analysis based on Soft Clustering through Dimensionality Reduction Technique

Saba Akmal; Hafiz Muhammad Shahzad Asif

No Detail Found!

Sentiment Analysis based on Soft Clustering through Dimensionality Reduction Technique

Author(s)

Saba Akmal Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan.

Hafiz Muhammad Shahzad Asif Department of Computer Science, University of Engineering and Technology Lahore, 54890, Pakistan.

Abstract

Clustering based sentiment analysis confers new directions to analyze real-world opinions without human participation and pre-tagged training data overhead. Clustering based techniques do not rely on linguistic information and more convenient as compared to other traditional machine learning techniques. Combining the dimensionality reduction techniques with clustering algorithms highly influence the computational cost and improve the performance of sentiment analysis. In this research, we applied Principal Component Analysis technique to reduce the size of features set. This reduced feature set improves binary K-means clustering results of sentiments analysis. In our experiments, we demonstrate the performance of the clustering system with a reduced feature set to provide high-quality sentiment analysis. However, K-mean clustering has its own limitations such as hard assignment and instability of results. To overcome the limitation of traditional Kmeans algorithm we applied soft clustering (Expectation maximization algorithm) approach which stabilizes clustering accuracy. This approach allows a soft assignment to cluster documents. Consequently, our experimental accuracy is 95% with standard deviation rate of 0.1% which is sufficient to apply the clustering technique in real-world applications.

Publication Details

Page(s) 630-644

DOI DOI not available

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 40, Issue: 3, Year: 2021

Request the full text from PASTIC

Keywords

principal component analysis, supervised learning, Gaussian mixture model, sentiment analysis, Symbolic Technique, Expectation Maximization, Unsupervised Learning, Dimensionality Reduction

References

Akmal Saba .2021 .Sentiment analysis based on soft clustering through dimensionality reduction technique. , 40(3) : 630-644.

Citations

Downloads

Views