The advancement of the Internet of Things (IoT) provides a set of new possibilities and challenges within the Industrial and Manufacturing environments, as well as into the Private Sector and the user daily life in our houses. In this context it is important to design and provide an IoT integrated system with a scalable architecture while maintaining a set of competitive costs and performance. This paper presents the development of low cost IoT‑based Smart Home Temperature and Humidity Monitoring System. The proposed architecture aims to demonstrate core IoT principles such as real‑time data collection, remote device control, and scalable architecture using low cost technologies, such as Arduino Uno R4 Wi‑Fi and ESP32 microcontrollers. The system successfully simulated appliance control ‑ e.g., radiators, extractor fans ‑ via LEDs and basic actuators, combined with a mobile application providing real‑time environmental data ‑ e.g., temperature, humidity, Carbon Monoxide (CO) levels ‑ and remote‑control functionality. Thanks to the proposed design the architecture is also inherently scalable, customizable and expandable, combining a modular approach with a customized mobile app and an user friendly interaction. Challenges included hardware compatibility, power management, and software integration, with further work on security features (i.e., cryptography algorithms) and cloud integration.

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Understanding public opinion at scale is both a scientific challenge and a practical necessity in the digital era, as the proliferation of online communication platforms has created unprecedented opportunities to monitor attitudes in near real time. Early work in subjectivity detection and semantic orientation laid the methodological foundations for automated sentiment extraction, focusing on distinguishing objective from subjective content and determining polarity. Contemporary applications, however, face far more complex requirements, demanding systems capable of processing massive, noisy, and dynamic data streams while integrating multimodal signals from text, images, audio, and video. This paper presents a historical review of sentiment analysis and opinion monitoring through the lens of artificial intelligence, tracing developments from the early 1990s to the present and classifying approaches from lexicon‑based heuristics to classical machine learning, deep neural architectures, transfer learning, and multimodal fusion, with an emphasis on both technical and conceptual advances. Extensive tables summarize algorithms, datasets, and case studies across various domains, including politics, finance, and entertainment, highlighting practical lessons and performance trends. The review also addresses pressing ethical concerns, including bias, fairness, and transparency, and considers the implications of rapidly evolving AI capabilities. We conclude by outlining future directions that emphasize adaptability, context awareness, and the seamless integration of emerging technologies into scalable and reliable opinion analysis systems.

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