This research endeavors to provide a thorough and insightful analysis of Internet of Things (IoT) tools within the context of smart homes. As the IoT continues to revolutionize the domestic landscape, understanding the integration, functionality, and user experience of these tools becomes paramount. The study surveys and categorizes prevalent IoT tools, encompassing sensors, processors, actuators, and databases. Integration capabilities are scrutinized, emphasizing interoperability and compatibility to ascertain the seamless incorporation of diverse IoT tools. Functional roles and contributions of each tool are dissected to illuminate their impact on automation, inter-connectivity, and overall control mechanisms in smart homes. The research extends its gaze to the user experience, exploring factors such as ease of use, reliability, and customization options, shaping the holistic perspective of IoT tools’ impact on residents. Realworld implementations and case studies provide tangible insights into practical applications, while surveys and interviews capture user perspectives, forming a comprehensive view of the challenges and limitations associated with these tools. This study contributes valuable insights for informed decision-making, empowering both users and developers to navigate the evolving landscape of IoT tools within the realm of smart homes.

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.