Analysis of Recognition of Twitter Posts using a Hybrid Machine Learning Approach

Abstract

Sentiment analysis of tweets has become a crucial task in understanding public opinion, sentiment trends, and brand perception. With the exponential growth of social media data, efficient and accurate sentiment analysis methods are essential. This research paper presents a comprehensive study on the design, simulation, and assessment of sentiment analysis of tweets using an improved machine learning methodology. The proposed methodology combines the strengths of natural language processing techniques and advanced machine learning algorithms to achieve superior performance in sentiment classificationIn the introduction, the paper outlines the significance of sentiment analysis and its applications in various domains. It discusses the challenges faced by traditional sentiment analysis approaches and highlights the need for more robust and accurate methodologies. The paper then delves into related works, exploring the existing state-of-the-art techniques and identifying gaps that the proposed methodology aims to address.The proposed methodology section details the steps involved in the sentiment analysis pipeline. It begins with data preprocessing, including tokenization, stop-word removal, and stemming. Feature extraction methods, such as TF-IDF and word embeddings, are explored and compared. Next, the paper presents an improved machine learning algorithm that combines ensemble learning and deep learning techniques. The model's architecture and training process are elaborated, along with the parameter tuning strategies to optimize performance.The results indicate that the proposed methodology outperforms traditional sentiment analysis approaches, achieving higher accuracy and robustness in sentiment classification. The paper highlights the model's strengths in handling sarcasm, irony, and context-specific language, which are common challenges in sentiment analysis. Furthermore, the efficiency of the proposed methodology is demonstrated through its ability to handle large-scale datasets in real-time. In conclusion, this research paper emphasizes the importance of sentiment analysis in understanding public opinion and its role in decision-making processes for businesses and governments. The proposed methodology exhibits promising results, making it a viable solution for sentiment analysis of tweets and other social media data. The study concludes by suggesting future research directions to further enhance sentiment analysis techniques and address emerging challenges in the field.