Online Social Media (OSM) like Facebook and Twitter has emerged as a powerful tool to express via text people’s opinions and feelings about the current surrounding events. Understanding the emotions at the fine-grained level of these expressed thoughts is important for system improvement. Such crucial insights cannot be completely obtained by doing AI-based big data sentiment analysis; hence, text-based emotion detection using AI in social media big data has become an upcoming area of Natural Language Processing research. It can be used in various fields such as understanding expressed emotions, human–computer interaction, data mining, online education, recommendation systems, and psychology. Even though the research work is ongoing in this domain, it still lacks a formal study that can give a qualitative (techniques used) and quantitative (contributions) literature overview. This study has considered 827 Scopus and 83 Web of Science research papers from the years 2005–2020 for the analysis. The qualitative review represents different emotion models, datasets, algorithms, and application domains of text-based emotion detection. The quantitative bibliometric review of contributions presents research details such as publications, volume, co-authorship networks, citation analysis, and demographic research distribution. In the end, challenges and probable solutions are showcased, which can provide future research directions in this area.

Current industry trends demand automation in every aspect, where machines could replace humans. Recent advancements in conversational agents have grabbed a lot of attention from industries, markets, and businesses. Building conversational agents that exhibit human communication characteristics is a need in today's marketplace. Thus, by accumulating emotions, we can build emotionally aware conversational agents. Emotion detection in text-based dialogues has turned into a pivotal component of conversational agents, enhancing their ability to understand and respond to users’ emotional states. This article extensively compares various artificial intelligence techniques adapted to text-based emotion detection for conversational agents. The study covers a wide range of methods, from machine learning models to cutting-edge pre-trained models and deep learning models. We evaluate the performance of these techniques on the benchmark unbalanced Topical-Chat and balanced Empathetic Dialogue datasets. This article offers an overview of the practical implications of emotion detection techniques in conversational systems and their impact on user response. The outcomes of this work contribute to the ongoing development of empathetic conversational agents, emphasizing natural human-machine interactions.

Energy demand forecasting is crucial to the creation of reliable and sustainable energy systems, given the rising global consumption and the increasing integration of renewable energy sources. In this study, we evaluate and compare a number of machine learning (ML) and deep learning (DL) techniques for energy consumption prediction. Our findings demonstrate the exceptional performance of DL models, particularly autoencoders, with an R² value of 0.9686. By using Optuna to adjust hyperparameters, we significantly enhance model performance, as evidenced by CatBoost’s rise from R² = 0.7415 to 0.8575. The study examines hybrid approaches, which blend traditional statistical methods like ARIMA with machine learning techniques like SVMs and ANNs, in addition to spatiotemporal modeling techniques. We consider significant external factors, such as population trends, economic indicators, and weather patterns. The primary challenges identified are computing loads, data quality issues, and scalability constraints. We recommend that future research give careful uncertainty quantification, a better comprehension of the dynamics of renewable energy integration, and interpretable models top priority in light of our findings. Our results indicate that, despite DL models’ superior predictive performance, optimized machine learning techniques provide a computationally cost-effective alternative. Because of this, they are particularly helpful in real-world energy planning applications where resources might be scarce.

This study aims to explore and understand the literature on digital transformation (DT) research that will lead to developing a conceptual and thematic structure of DT management. The research approach employed a hybrid approach of bibliometric analysis and a structured review of DT management research studies from 2017 to 2023. Although systematic reviews and bibliometric analysis are distinct methods, they complement one another in research by enabling the synthesis and analysis of existing literature. Scopus and Web of Science (WoS) were selected as the bibliographic databases for this study since they are generally accepted as the most comprehensive data sources for various purposes. Findings revealed 186 publications during these periods, with the top three publications being the Journal of Business Research, Business Horizons and California Business Review. Top-ranking researchers hailed from Germany, Japan and Spain. Keywords that appeared were innovation, digital transformation, artificial intelligence and DT. In the management area, there are fewer studies conceptualising DT management that led to this study forming a conceptual framework using a qualitative, interpretative and thematic analysis approach. These findings are relevant for understanding the phenomenon of DT and DT management. The study contributes to the emerging body of knowledge in management and DT.