Optimizing Natural Language Processing Applications for Sentiment Analysis

Abstract

Recent technological advances have stimulated the exponential growth of social network data, driving an increase in research into sentiment analysis. Thus, studies exploring the intersection of Natural Language Processing and social network analysis are playing an important role, specially those one focused on heuristic approaches and the integration of algorithms with machine learning. This work centers on the application of sentiment analysis techniques, employing algorithms such as Logistic Regression and Support Vector Machines. The analyses were performed on datasets comprising 5,000 and 10,000 tweets, and our findings reveal the efficient performance of Logistic Regression in comparison with other approach. Logistc Regression improved the performed in almost all measures, with emphasis to accuracy, recall and F1-Score.