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

  • Authors

    • Srishti Sudhir Patil Student, Third Year B.Tech.: Computer Science and Engineering, Department of Computer Engineering and Technology, ‎Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Kothrud, Pune: 411038, Maharashtra State, India https://orcid.org/0009-0009-5517-6787
    • Vaishali Pankaj Suryawanshi Assistant Professor, Department of Computer Engineering and Technology, ‎Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Kothrud, Pune: 411038, Maharashtra State, India https://orcid.org/0000-0002-4951-0760
    • Sudhir Madhav Patil Associate Professor, Department of Manufacturing Engineering and Industrial Management, ‎COEP Technological University (COEP Tech), Chhatrapati Shivajinagar, Pune: 411005, Maharashtra State, India https://orcid.org/0000-0002-9898-0793
    • Sheetal Pravin Girase Assistant Professor, Department of Computer Engineering and Technology, ‎Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Kothrud, Pune: 411038, Maharashtra State, India
    • Dhananjay Abhilash Bhagat Assistant Professor, Department of Computer Engineering and Technology, ‎Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Kothrud, Pune: 411038, Maharashtra State, India
    https://doi.org/10.14419/mhv83077

    Received date: April 14, 2025

    Accepted date: June 3, 2025

    Published date: June 9, 2025

  • Big Data; Deep Learning; Machine Learning; Natural Language Processing; Sentiment Analysis; Social Media

  • Abstract

    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.

    Author Biography

    • Srishti Sudhir Patil, Student, Third Year B.Tech.: Computer Science and Engineering, Department of Computer Engineering and Technology, ‎Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Kothrud, Pune: 411038, Maharashtra State, India

      I am a passionate and research-oriented third-year B.Tech. Computer Science and Engineering student at the Department of Computer Engineering and Technology, Dr. Vishwanath Karad MIT World Peace University (MIT-WPU), Pune. Maharashtra State, INDIA.

      My academic journey is complemented by my strong interest in interdisciplinary research and real-world applications of computational intelligence. I have co-authored two SCOPUS-indexed research papers and contributed to two more—one recently accepted and another currently under review. My work spans areas such as metaheuristic algorithms, production scheduling systems, industrial automation for MSMEs, and sentiment analysis using big data.

      Skill Sets and Strengths:
      Proficient in Python, Java, SQL, and R
      Data visualization using Power BI and MS Excel
      Algorithm design and optimization
      Experience in integrating software tools for industrial applications
      Strong analytical and problem-solving abilities
      Effective technical communication and collaborative research skills

      Areas of Interest:
      Artificial Intelligence and Machine Learning
      Operations Research and Scheduling Algorithms
      Data Analytics and Business Intelligence
      Industrial Automation and Smart Manufacturing
      Natural Language Processing and Sentiment Analysis

      I aim to bridge the gap between academic research and industrial applications through innovative and impactful solutions, particularly focusing on MSMEs and societal challenges.

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    Patil, S. S., Suryawanshi, V. P. ., Patil, S. M., Girase , S. P. ., & Bhagat , D. A. . (2025). Review of sentiment analysis in social media using big data: ‎techniques, tools, and frameworks. International Journal of Basic and Applied Sciences, 14(2), 34-48. https://doi.org/10.14419/mhv83077