Review of sentiment analysis in social media using big data: ‎techniques, tools, and frameworks - Patil, Srishti Sudhir; Suryawanshi, Vaishali Pankaj; Patil, Sudhir Madhav; Girase , Sheetal Pravin; Bhagat , Dhananjay Abhilash (International Journal of Basic and Applied Sciences, 2025)
Sentiment analysis on social media has emerged as a vital research area due to the growing volume of user-generated content and the in-‎creasing reliance on data-driven decision-making. The adoption of big data technologies has greatly improved sentiment analysis by enabling the rapid processing of unstructured big data. This review presents an in-depth analysis of sentiment analysis methodologies, covering ‎both conventional machine learning (ML) techniques-such as Naïve Bayes, Support Vector Machines, Decision Trees, and Random Forest ‎and advanced deep learning (DL) models, including Recurrent Neural Networks, Long Short-Term Memory Networks, Convolutional Neu-‎ral Networks, and Transformer-based architectures like Bidirectional Encoder Representations from Transformers (BERT) and Generative ‎Pre-trained Transformers (GPT). Furthermore, it examines big data frameworks like Hadoop, Apache Spark, and Apache Flink, along with ‎Natural Language Processing (NLP) tools such as the Natural Language Toolkit (NLTK), spaCy, TextBlob, and Stanford NLP. The paper ‎also discusses ML/DL frameworks like Scikit-learn, TensorFlow, PyTorch, and Keras, along with cloud and edge computing solutions like ‎Google Cloud Artificial Intelligence (AI), Amazon Web Services (AWS) Comprehend, and Edge AI (NVIDIA Jetson). Despite technological advancements, several challenges persist, including issues related to data quality, real-time processing limitations, multilingual analysis ‎complexities, and ethical concerns regarding bias and privacy. The field is also witnessing promising developments, such as Explainable ‎Artificial Intelligence (XAI), federated learning, edge computing, and quantum computing, which offer new directions for future research ‎and practical implementations. This review provides researchers and professionals with valuable insights, outlining potential improvements ‎in sentiment analysis techniques to enhance accuracy, scalability, and ethical considerations across various sectors, including business, ‎healthcare, and smart manufacturing‎.