AWS Powered Sentiment Analysis

The paper discusses the results of a study which extracts meaningful insights from vast amounts of unstructured text has become critical for businesses and organizations. Sentiment analysis, a Natural Language Processing (NLP) technique, is used to determine whether a piece of text conveys positive, negative, or neutral sentiment. By leveraging Amazon Web Services (AWS) such as Amazon Comprehend for language processing, AWS Lambda for serverless execution, and Amazon S3 for data storage, the system can process large datasets efficiently. This feedback can guide the development of new products or improvements to existing ones, aligning them more closely with customer preferences. The dataset includes a sample of 10,000 tweets, pre-processed to remove noise and irrelevant content. The results indicate that while deep learning models, particularly Long Short-Term Memory (LSTM) networks, outperform traditional methods in terms of accuracy, challenges remain in dealing with sarcasm and ambiguous language. The findings suggest that sentiment analysis can be a valuable tool for understanding public sentiment in real-time, with potential applications in marketing, political analysis, and customer feedback systems. However, improvements in model handling of contextual nuances are needed for more precise sentiment classification. Keywords – Amazon Web Service (AWS), Sentiment Analysis, Hidden emotions