Machine Leaning-Driven Approaches to Enhance Sentiment Classification in Social Media

Abstract

Analyzing tweets for sentiment is becoming an increasingly significant component of understanding public opinion, sentiment trends, and how people perceive companies. The amount of social media data is increasing, and with it, the necessity for accurate and efficient sentiment analysis methods. This article presents the results of a comprehensive study that aimed to improve the machine learning method for sentiment analysis of tweets through planning, modeling, and assessment. We recommend combining cutting-edge machine learning algorithms with the most effective natural language processing techniques for improved sentiment categorization outcomes. In the first part of the article, the authors discuss the importance and applications of sentiment analysis in many domains. It highlights the need for more accurate and dependable approach by addressing the issues with traditional sentiment analysis methods. Following this, the essay delves into relevant research, examining existing state-of-the-art methodologies and identifying gaps that the proposed methodology aims to cure. The workflow for sentiment analysis is detailed in the methodology section. The first steps in preparing the data include tokenization, stemming, and stop- word removal. Two feature extraction methods that are examined and compared are word embeddings and TF-IDF. The paper continues by introducing an improved machine learning method that combines deep learning with ensemble learning. In addition to elaborating on the model's architecture, training procedure, and tactics for improving performance parameters, the findings demonstrate that the proposed technique outperforms standard sentiment analysis approaches in terms of accuracy and resilience in sentiment categorization. The paper highlights the model's proficiency in handling sentiment analysis challenges, including language that is particular to context, irony, and sarcasm. The ability to handle large datasets in real-time is another proof of how effective the method is. In its last section, this research piece emphasizes the value of sentiment analysis for understanding public opinion and how it plays a role in business and government decision-making. Applying the proposed techniques to the analysis of social media data, including the sentiment of tweets, has shown promising results. Finally, the report suggests future research directions for addressing emerging issues in the sentiment analysis field and improving existing approaches.