Daily Archives: June 26, 2025

Authors: Mr. Satish Yadav, Ashish Khandagale, Dr. Jasbir Kaur, Ms. Sandhya Thakar, MS. Ifra Kampoo

Abstract: The exponential growth of social media platforms has generated unprecedented volumes of user-generated content, creating vast repositories of public opinion and sentiment. This research investigates the application of sentiment analysis techniques to social media big data, examining methodologies for extracting, processing, and analyzing emotional insights from large-scale social media datasets. Through a comprehensive review of machine learning approaches, natural language processing techniques, and big data analytics frameworks, this study evaluates the effectiveness of various sentiment classification models when applied to Twitter, Facebook, and Instagram data. Our findings demonstrate that hybrid approaches combining lexicon-based methods with deep learning architectures achieve superior accuracy rates of 89.3% compared to traditional rule-based systems. The research also addresses critical challenges including data preprocessing, feature engineering, and scalability issues inherent in social media sentiment analysis. The implications of this work extend to business intelligence, political analysis, brand monitoring, and public health surveillance applications.

Published by: vikaspatanker

Authors: Kalpesh Desai, Komal Yadav, Jasbir Kaur, Suraj Kanal, Sandhya Thakkar

Abstract: The Internet of Things (IoT) based smart agriculture system is an emerging technology that uses sensors, gateways, cloud platforms, and mobile applications to provide real-time data on weather, soil moisture, crop growth, and livestock health to farmers. This research paper focuses on developing and implementing an IoT-based smart agriculture system. The system offers several benefits: increased efficiency, improved resource management, enhanced crop quality, better decision-making, and remote monitoring. However, potential negative impacts, such as cost, technical skills, dependence on technology, data privacy and security, and environmental impact, must be considered. Careful planning, implementation, and monitoring can help to mitigate these risks and ensure that smart agriculture systems are sustainable and effective. This research aims to give an overview of how predictive analysis and Internet of Things (IoT) devices, along with cloud management and security systems, can be used in agriculture to support multiple crops. It also takes into account the experiences of farmers and highlights the challenges and difficulties that may arise when introducing modern technology into traditional farming practices. By utilizing statistical and quantitative methods, this research seeks to bring about significant and positive changes in the current agriculture system. In simpler terms, this study explores how smart farming can enhance food production, resource management, and labor efficiency, while acknowledging the challenges and benefits of integrating modern technology into traditional farming practices.

 

 

Published by: vikaspatanker