Machine Learning Methodologies Based Improved Classification System for Sentiment Analysis of Tweets

Abstract

An increasingly important part of studying public opinion, sentiment patterns, and how people see brands is analyzing tweets for sentiment. The need for effective and precise sentiment analysis techniques is growing in tandem with the volume of social media data. This article details an extensive investigation into the planning, modeling, and evaluation of an enhanced machine learning approach to sentiment analysis of tweets. In order to achieve better results in sentiment classification, the suggested approach integrates the best of natural language processing methods with state-of-the-art machine learning algorithms. The paper begins by outlining the relevance and uses of sentiment analysis in different fields. It draws attention to the necessity for more reliable and precise methodology by discussing the problems with conventional sentiment analysis techniques. After that, the article dives into related research, looking at current state-of-the-art methods and finding holes that the suggested approach intends to fill. The methodology part explains how the sentiment analysis pipeline works. Tokenization, stop-word removal, and stemming are part of the data preparation steps that start it all. Word embeddings and TF-IDF are two of the feature extractions approaches that are investigated and contrasted. An enhanced machine learning algorithm integrating deep learning and ensemble learning is subsequently introduced in the article. The results show that the suggested methodology achieves better accuracy and resilience in sentiment classification than traditional sentiment analysis approaches, and it also elaborates on the model's architecture, training process, and strategies for optimizing performance parameters. The article emphasizes the model's capabilities in dealing with sentiment analysis problems such as context-specific language, sarcasm, and irony. Its capacity to manage massive datasets in real-time further demonstrates the efficacy of the suggested technique. This study article concludes by stressing the significance of sentiment analysis in gaining insight into public opinion and its function in governmental and corporate decision-making. Results from using the suggested methods to analyze the sentiment of tweets and other social media data are encouraging. In its last section, the paper proposes avenues for additional investigation into how to improve sentiment analysis methods and deal with new problems that are cropping up in the industry.