Using Sort-Union to Enhance Economically-Efficient Sentiment Stream Analysis

Sentiment drifts due to people changing their opinions instantly on microblogs e.g. Twitter, are a major challenge in sentiment analysis. In this paper, we have developed a method that selects most frequent messages from a relevant message set constructed using state-of-the-art sampling approaches. Our proposed technique increases the robustness of the classifier against sentiment drifts. Experiments conducted on three publicly available standard Twitter datasets reveal that the modified version performs better in terms of reduction in training resources, error minimization and execution time.