Improving Twitter Sentiment Analysis Efficiency with SVM-PSO Classification and EFWS Heuristic

Abstract

In the context of the increasingly interconnected world shaped by microblogging services like Twitter, sentiment analysis holds vital significance. The sway of others' sentiments and opinions over our perceptions is now undeniable, transcending individual viewpoints. Our investigation extends the boundaries of previous endeavours in Twitter sentiment analysis, adopting a strategic utilization of Distant Supervision. However, the existing method deals with challenges in analyzing the immense tweet volume due to computational limitations. To overcome this challenge, novel strategies are led in, aimed at expediting the sentiment analysis process. This involves connecting the subjectivity embedded within tweets to precisely select training samples. Additionally, the conceptual framework of EFWS (Effective Word Score) is introduced, intricately interlinked with tweets. This innovative heuristic, derived from polarity assessments of frequently encountered words, emerges as a catalyst for elevating the efficiency of sentiment classification through conventional machine learning algorithms. Experimental investigations encompass a dataset of 1.6 million tweets, showcasing the superior efficiency and heightened precision of the methods compared to prior approaches. Noteworthy is the overall accuracy of approximately 80%, which ascends to around 85% with the integration of the EFWS heuristic, utilizing a training dataset of 100K tweets – a dataset merely half the magnitude of the baseline model. Remarkably, the proposed model elevates accuracy by 2-3% relative to the baseline, all while accelerating training, achieving double the speed of the baseline. Furthermore, the classification leverages Support Vector Machine (SVM) with tuning via Particle Swarm Optimization (PSO), further enhancing performance.