Statistical Analysis and Accuracy Assessment of Improved Machine Learning Based Opinion Mining Framework

Abstract

Sentiment analysis, also known as opinion mining, plays a crucial role in understanding and extracting valuable insights from textual data in various domains, including social media, customer feedback, and product reviews. This research presents an in-depth examination of an improved machine learning-based sentiment analysis framework, focusing on its statistical analysis and accuracy assessment. The research begins by introducing the framework's architecture, which incorporates advanced machine learning algorithms and natural language processing techniques. These enhancements aim to provide a more nuanced and context-aware sentiment analysis, addressing the limitations of traditional approaches. To evaluate the performance of the proposed framework, a comprehensive statistical analysis is conducted. Various statistical metrics, such as precision, recall, F1-score, and accuracy, are employed to assess its effectiveness in classifying text sentiments accurately. Additionally, the study explores the impact of different feature engineering and pre-processing techniques on model performance. The results of this study demonstrate the significant improvements achieved by the enhanced sentiment analysis framework in terms of accuracy and reliability. The statistical analysis confirms its superior performance in capturing subtle sentiment nuances, making it a valuable tool for applications requiring precise sentiment understanding. In conclusion, this research contributes to the field of sentiment analysis by presenting an improved machine learning-based framework and conducting a rigorous statistical assessment of its accuracy. The findings provide valuable insights for researchers and practitioners seeking to enhance sentiment analysis techniques and apply them effectively in various domains..