Sentiment Expression Across Cultures: A Global Twitter Analysis

Sentiment analysis has shown to be a useful tool for gauging public opinion in a variety of fields. It has proven useful in a variety of fields, including Mental Health Monitoring, Entertainment Industry, Sports Analytics, Cybersecurity, Human Resources, E-commerce, Legal and Judiciary. Given Twitter's enormous and diverse user base, sentiment analysis of tweets has received substantial research. To achieve this purpose, the suggested architecture utilizes effective feature optimization and deep learning-based sentiment analysis. The sentiment dataset has been used to assess the effectiveness of the suggested RNN and attention models. First, the accessible data is cleaned and filtered. A Recurrent Neural Network (RNN) with attention mechanism is used for classifying sentiment into positive, negative, and neutral categories. This model combines an RNN with attention mechanisms such as multi-head and selfattention. Finally, the suggested model's performance is evaluated in comparison to current models. The suggested model accurately predicts performance indicators with an accuracy of 87.76%.