Result: Review of sentiment analysis in social media using big data: techniques, tools, and frameworks
Further Information
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.