Analyzing Twitter Sentiments: Comparative Evaluation of Classification Models Using Cloud

This study explores the use of Twitter review sentiment analysis with Azure Cloud services. Sentiment analysis is a Natural Language Processing approach used to extract the sentiment from textual input. Through the use of Azure’s cognitive and machine learning capabilities, the research focuses on sentiment analysis of tweets that are sourced via Twitter. First, the text is preprocessed to clean and prepare the data. Next, features are extracted to find important aspects that indicate sentiment. Next, tweets are categorized into categories of positive, negative, or neutral sentiment using machine learning algorithms. The study evaluates the effectiveness and precision of sentiment analysis made possible by Azure Cloud, highlighting its possible uses in customer sentiment tracking, brand impression analysis, and social media monitoring. This study adds to our understanding of how to use cloud-based tools for scalable and effective sentiment analysis applications, especially when analyzing social media data.