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@article{186752,
        author = {Dr.Nilesh N Thorat},
        title = {A Predictive Analytics Platform for Social Media Sentiment Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3043-3049},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186752},
        abstract = {In the contemporary digital economy, the ability to understand and predict customer sentiment is a critical driver of business strategy and growth. Businesses increasingly depend on customer reviews and social media posts, but face significant hurdles in analyzing this vast, unstructured data, including the presence of fake reviews and multilingual content111. This paper introduces a comprehensive Predictive Analytics Platform designed to automate sentiment analysis and forecast market trends. The system architecture integrates data collection from diverse APIs, followed by robust preprocessing using Natural Language Processing (NLP)2. At its core, the platform utilizes a novel hybrid machine learning model combining Random Forest and Long Short-Term Memory (LSTM) to classify sentiment from labeled training data3. Implemented with an interactive dashboard built on Streamlit, the system achieves 85% accuracy in its predictions. By delivering dynamic data visualizations and predictive insights, the platform equips businesses with the tools for rapid, data-driven decision-making in a dynamic environment.},
        keywords = {Sentiment Analysis, Predictive Analytics, Machine Learning, LSTM, Random Forest, Natural Language Processing, Social Media Analytics, TensorFlow, Streamlit.},
        month = {November},
        }