The field of sentiment analysis is a crucial aspect of natural language processing (NPL) and is essential in discovering the emotional undertones within the text data and, hence, capturing public sentiments over a variety of issues. In this regard, this study suggests a deep learning technique for sentiment categorization on a Twitter dataset that is based on Long Short-Term Memory (LSTM) networks. Preprocessing is done comprehensively, feature extraction is done through a bag of words method, and 80-20 data is split using training and testing. The experimental findings demonstrate that the LSTM model outperforms the conventional models, such as SVM and Naïve Bayes, with an F1-score of 99.46%, accuracy of 99.13%, precision of 99.45%, and recall of 99.25%. Additionally, AUC-ROC and PR curves validate the model’s effectiveness. Although, it performs well the model consumes heavy computational resources and longer training time. In summary, the results show that deep learning performs well in sentiment analysis and can be used to social media monitoring, customer feedback evaluation, market sentiment analysis, etc.

Effective task scheduling in cloud computing is crucial for optimizing system performance and resource utilization. Traditional scheduling methods often struggle to adapt to the dynamic and complex nature of cloud environments, where workloads, resource availability, and task requirements constantly change. Swarm intelligence-based optimization algorithms, such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Artificial Bee Colony (ABC), offer a promising solution by mimicking natural processes to explore large search spaces efficiently. These algorithms are effective in balancing multiple objectives, including minimizing execution time, reducing energy consumption, and ensuring fairness in resource allocation. They also enhance system scalability, which is vital for modern cloud infrastructures. However, challenges remain, including slow convergence speeds, complex parameter tuning, and integration with existing cloud frameworks. Addressing these issues will be essential for the practical implementation of swarm intelligence in cloud task scheduling, helping to improve resource management and overall system performance.