An unsupervised fuzzy clustering method for twitter sentiment analysis
Hima Suresh, S. Gladston Raj · 2016
Cluster based techniques on sentiment analysis is a novel approach for analyzing sentiments expressed in social media sites. It is a main task of exploratory data mining, and a common technique used in machine learning. In contrast to supervised learning technique, the cluster based techniques produce essentially accurate experimental results without manual processing, linguistic knowledge or training time. This paper presents a novel fuzzy clustering model to analyze twitter feeds regarding the sentiments of a particular brand using the real dataset collected over a period of one year. Then a comparative analysis is made with the existing partitioning clustering techniques namely K Means and Expectation Maximization algorithms based on metrics namely accuracy, precision, recall and execution time. According to the experimental analysis, the proposed approach is tested to be practicable in performing high quality twitter sentiment analysis results.