Event Impact Analysis Using Machine Learning
Mulla Sana1, Mutayala Sridevi2,
1Student Department of Masters of Computer Applications & BMS Institute of Technology and Management
2Professor Department of Masters of Computer Applications & BMS Institute of Technology and Management
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Abstract - The "Event Impact Analysis using Machine Learning" project presents an innovative and comprehensive framework for evaluating the repercussions of significant events. This study presents a machine learning model designed to classify feedback into positive or negative sentiments based on relevant parameters, accompanied by comprehensive parameter visualizations. The aim is to create an effective system that accurately categorizes feedback sentiment while providing insightful graphical representations of key parameters. The model employs diverse machine learning algorithms, including advanced natural language processing techniques, to extract informative features from textual feedback. These features are then utilized to train a sentiment classification model capable of distinguishing between positive and negative sentiments.
Key parameters contributing to sentiment classification are identified and visually presented through informative graphs and charts. These visualizations offer an intuitive understanding of the relationships between parameters and sentiment, enhancing the interpretability of the model's decision-making process. Real-world feedback datasets are utilized for model training and evaluation. Standard metrics such as accuracy, precision, recall, and F1-score are employed to assess the model's performance in sentiment classification. This study contributes to the field of sentiment analysis by combining accurate sentiment classification with parameter visualization.
The resulting insights offer businesses a deeper understanding of customer feedback, aiding in improved decision-making and enhanced user experiences.
Key Words: Event Analysis, Feedback Classification, Parameter Visualization, Natural Language Processing, Decision-Making.
