Abstract: Twitter sentiment analysis has become very popular because of its usage. Having said that, the stable, optimized Twitter sentiment classification remains elusive due to several issues: heavy class imbalance, domain restrictions of the learning algorithms, the representational richness issues for sentiment cues, and the use of diverse loops. These issues are problematic since many forms of social media analytics rely on accurate underlying Twitter sentiments. Accordingly, a simple yet effective method is proposed for Twitter sentiment analysis. This also does a comparison in both R an python and uses the new code to cover up the existing issues. Experiment results reveal that the proposed approach is more accurate and balanced in its predictions across sentiment classes, as compared to various comparison tools and algorithms. Consequently, this method is better able to reflect strong positive and negative sentiments from users. Considering the importance of Twitter as one of the premier social media platforms, the results have important implications for social media analytics and social intelligence.
Keywords: Python, R, Sentimental Analysis, Twitter.
| DOI: 10.17148/IJARCCE.2021.10312