Browsing by Author "Oluwatobi Akanbi JOHNSON"

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    Improved Sentimental Response System for Classifying Emergency Incidence Through Hybridized Minning Techniques
    (2024-12) Oluwatobi Akanbi JOHNSON
    Emergency occurrences can be caused by both natural disasters and human error. This study addresses the classification of emergency incidence, stemming from both natural disasters and human errors, emphasizing the critical need for swift response and effective mitigation. Governments typically implement measures to mitigate negative effects, with outcomes dependent on their responsiveness. The research aims to enhance sentiment analysis for emergency incidence through a hybridized mining technique. The system combines Natural Language Processing and Bayesian belief learning, focusing on data mining, machine learning, and NLP for effective classification and sentiment analysis. Social media data from Facebook is gathered using the Facebook API and Graph function 'Requests' for training. Pre- processing involves eliminating unwanted characters and transforming text into lowercase. Experimental analysis involves 450 data samples with four characteristics, creating a multivariate time series dataset for classification tasks. Python with the requests library and Graph API is used for live data capture, while MySQL manages the backend database, and XML and PHP handle the frontend for sentimental response. The study unveils a linear dimension in the classification algorithm, transforming non-linear textual data during pre- processing. Probability computations for incidence parameters and input intervals rely on frequency distribution from emergency observations. Experimental scenarios instill confidence in the improved framework, incorporating supervised learning into NLP for improved precision. The system achieves over 90.93% efficiency in signal precision, a substantial enhancement compared to existing models. Performance evaluation involves using emergency datasets for training (75%) and testing (25%), demonstrating the system's high precision through a confusion matrix. The improved sentimental response system represents a significant advancement, leveraging social media data for proactive emergency management. With a precision rate exceeding 90.93%, the system adeptly identifies and categorizes emergency signals, enabling timely and targeted response strategies. Keywords: Emergency, Hybrid, Incidences, Minning ,Response, Sentiment, Socialmedia Word Counts: 297