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Sentiment analysis is a natural language processing (NLP) technique used to decide if the underlying sentiment is positive, negative, or neutral. Subjective information from the text can be extracted using sentiment analysis by recognizing its context and position. Data from a variety of sources, like social network comments, news stories, consumer reviews, and more, can be used for sentiment analysis. Sentiment analysis uses different algorithms to analyze words, phrases, and context available in text and different procedures to determine the overall sentiment communicated. There are various ways in which sentiment analysis is performed, ranging from rule-based methods that use lists of positive and negative terms as labeled data for training machine learning algorithms to building classifiers. Understanding social sentiment, underlying intents, and responses to various characteristics of humans can be done with the help of sentiment analysis, which helps in decision-making. The primary goal of this work is to provide the audience with the knowledge needed to understand sentiment analysis, highlight potential opportunities and challenges, and investigate recent studies that have been published in reputable resources focusing on the field of sentiment analysis in NLP.

The healthcare sector produces an enormous amount of complicated data from several sources, such as health monitoring systems, medical devices, and electronic health records. Big data analytics may improve healthcare by enabling more effective decision-making, improving patient outcomes, and reducing costs. To improve the operational efficiency of healthcare organizations, scientific studies must search for the standardization and integration of data analysis equipment and methods. This systematic literature review aims to provide current insights on the topic by analyzing a total of 60 relevant articles published between 2017 and 2023. The review explores the challenges and opportunities in using big data in healthcare, including data security, privacy, data quality, interoperability, and ethical considerations. The article also explores big data analytics' potential uses in healthcare, such as personalized treatment, disease prediction and prevention, and population health management. It provides significant insights for healthcare providers, researchers, and practitioners to make evidence-based decisions, as well as underlines the need for more research in this area to fully realize the promise of big data in healthcare.

Emotions play a vital role in education. Technological advancement in computer vision using deep learning models has improved automatic emotion recognition. In this study, a real-time automatic emotion recognition system is developed incorporating novel salient facial features for classroom assessment using a deep learning model. The proposed novel facial features for each emotion are initially detected using HOG for face recognition, and automatic emotion recognition is then performed by training a convolutional neural network (CNN) that takes real-time input from a camera deployed in the classroom. The proposed emotion recognition system will analyze the facial expressions of each student during learning. The selected emotional states are happiness, sadness, and fear along with the cognitive–emotional states of satisfaction, dissatisfaction, and concentration. The selected emotional states are tested against selected variables gender, department, lecture time, seating positions, and the difficulty of a subject. The proposed system contributes to improve classroom learning.