Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194879,
        author = {Sarang Pratap and Ayush Patil and Varad Patil and Uma K.S.},
        title = {Sentimental Analysis from Social Media Posts for Disaster Management},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6462-6470},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194879},
        abstract = {During natural disasters and emergency situations, social media platforms become an important channel where people share real-time information, opinions, and requests for help. These posts often contain valuable insights about the situation on the ground, including public emotions, urgent needs, and updates from affected areas. However, the large volume of unstructured data generated during such events makes it difficult for authorities to manually monitor and interpret the information in a timely manner. This project proposes a system that performs sentiment analysis on social media posts related to disaster events in order to understand public reactions and identify critical situations. Using Natural Language Processing (NLP) techniques and machine learning algorithms, the collected tweets are processed, cleaned, and classified into different sentiment categories such as positive, negative, and neutral. The system helps in highlighting areas of concern, panic, or distress expressed by users online. By analyzing these sentiment patterns, disaster management authorities can gain quicker insights into public response and potentially improve decision-making and resource allocation during emergencies.},
        keywords = {Sentiment Analysis, Disaster Management, Twitter Streaming API, Natural Language Processing, Machine Learning, Public Sentiment Monitoring},
        month = {March},
        }