In today’s digital age dominated by social media, understanding public sentiment through Twitter analysis has become imperative. With a staggering 100 million active users on platforms like Twitter and an influx of 572,000 new accounts daily, the vast reservoir of user-generated content underscores the necessity for advanced sentiment analysis tools. This study delves into the realm of sentiment analysis techniques on Twitter, with a particular emphasis on employing Machine Learning (ML) methods. The proposed framework harnesses the power of Natural Language Processing (NLP) and Deep Learning architectures, specifically advocating for a synergistic blend of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Additionally, it explores the efficacy of traditional ML algorithms such as Support Vector Machines (SVM), Random Forest, and Multi-Layer Perceptron (MLP) in this context. The study’s findings illuminate diverse performance metrics across the employed models. While SVM exhibits moderate accuracy, it grapples with challenges in recall and F1-score for sentiment class 1. Conversely, the CNN-LSTM model emerges as a standout performer, boasting impressive accuracy rates of 97% and 98% respectively. Notably, this model excels in sentiment classification across all classes, underscoring its efficacy in discerning nuanced sentiment nuances within tweets. Furthermore, the study underscores the critical importance of judiciously selecting ML algorithms tailored to the intricacies of Twitter sentiment analysis. By leveraging advanced NLP techniques and deep learning architectures, researchers and practitioners can glean deeper insights into the dynamic landscape of public sentiment on social media platforms like Twitter. Such insights hold significant implications for diverse domains, including marketing, brand management, and public opinion analysis.

SDN (software defined networks) is a programmable network architecture that divides the forwarding plane and control plane. It can centrally manage the network through a software program, i.e., controller. Multiple controllers are required to manage the current software defined WAN. Placing multiple controllers in a network is known as controller placement problem (CPP). Only one controller is not capable to handle the scalability and reliability issues. To tackle these issues, multiple controllers are required. Efficient deployment of controllers in SDN is used to improve the performance and reliability of the network. To the best of our knowledge, this is the first attempt to minimize the total average latency of reliable SDN along with the implementation of TLBO and PSO algorithms to solve CPP. Our experimental results show that TLBO outperforms PSO for publicly available topologies.

A new and simple optimization algorithm known as Varna-based Optimization (VBO) is introduced in this paper for solving optimization problems. It is inspired by the human-society structure and human behavior. Varna (a Sanskrit word, which means Class) is decided by people’s Karma (a Sanskrit word, which means Action), not by their birth. The performance of the proposed method is examined by experimenting it on six unconstrained, and five constrained benchmark functions having different characteristics. Its results are compared with other well-known optimization methods (PSO, TLBO, and Jaya) for multi-dimensional numeric problems. Our experimental results show that the VBO outperforms other optimization algorithms and have proved the better effectiveness of the proposed algorithm.