Published Paper Details:

Sentiment analysis, commonly known as opinion mining, has emerged as a crucial aspect of Natural Language Processing (NLP), particularly in analyzing consumer feedback. This study investigates a variety of advanced techniques and methodologies employed in the sentiment analysis of online product reviews. We explore several approaches, including feature-wise sentiment analysis, aspect-based sentiment analysis (ABSA), and innovative implementations of deep learning and machine learning techniques such as BERT-based sentiment analysis, document-level sentiment assessment, and convolutional neural networks (CNNs) combined with ABSA. These methodologies aim to accurately capture sentiments, classify sentiment polarity, recognize sarcasm, and analyze the specific aspects and features highlighted in customer reviews. Through a rigorous evaluation of these techniques, we demonstrate their efficacy in precisely categorizing sentiments and extracting meaningful insights from customer opinions. The findings of this research significantly advance the field of sentiment analysis across various applications, including online product evaluations, restaurant assessments, mobile app feedback, and broader customer opinion analysis. By leveraging sentiment analysis, organizations can make data-driven decisions, refine their product strategies, and enhance customer satisfaction. This study underscores the importance of integrating multiple input techniques to enrich sentiment analysis capabilities. The key terms associated with this research include sentiment analysis, opinion mining, online product reviews, feature-wise sentiment analysis, ABSA, deep learning, machine learning, BERT, document-level sentiment analysis, CNN, and customer feedback analysis. In every pandemic that humans have faced, there are no effective and efficient applications for detecting face masks, which are highly required for transportation facility, densely populated areas, large- scale industries, and other organizations to ensure safety. During pandemic times, the people are advised to wear a face mask in the public place and wherever they meet with other people. However, the authorities are facing some challenges in monitoring a huge population with a different habit. The government requires an efficient solution which will validly manage the execution of the law, which sets up with the availability of the data rapidly and accurately. Face mask detection is a systematic procedure of finding whether a person is sporting a mask or not. Actually, the model uses reverse engineering technique of face detection where the face is detected using numerous machine learning algorithms for the purpose of security, verification, and surveillance. This Python training model for face mask detector, accepts the input datasets, loads the images, and pre-processes the images, and labels them using TensorFlow and Keras modules. Using your webcam, the system developed applies face mask detection correctly to each frame in the live video stream. Then the system will detect who is wearing mask and who is not wearing mask in real-time video stream using a webcam.