Handling High Dimensional Word Patterns as Features by Ensemble Learning for Opinion Valuation from Twitter Streams
Keywords:Feature Optimization, Machine-Learning, KS-Test, Term-Occurrence, Naïve Bayes, Wilcoxon signed-rank, Fuzzy-c Means, Handling Dimensionality
The stream of over billion tweets are often influence by ambiguity. Due to volume and ambiguity these tweets reflects high dimensionality. The curse of high dimensionality causes more false alarming in detection of sentiment polarity using supervised learning. Though the many of contemporary contributions portrayed novel ensemble classification strategies, limited to handle the volume of data constraints or ambiguity constrained. This manuscript endeavored to portray a novel ensemble classification model that uses fusion of diversified measures to find optimal features, and a novel clustering method fuzzy c-means clustering technique to handle the high dimensionality. The resultant clusters are further used as input training corpus for classification, such that each cluster is used as input training corpus for individual classifier. The experimental study has carried by multi label four fold cross validation. In order to scale the performance, the results obtained for cross validation metrics for proposed model titled “ELOV” and the contemporary contributions of ensemble models. The performance analysis projecting that the proposed model is outperforming the contemporary contributions.
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