Classifying Twitter Sentiment on Multi- Levels using A Hybrid Machine Learning Model
Keywords:
Social network, Twitter, sentiment classification, tweets, hybrid machine learning (ML), SNN NBAbstract
Social networking sites like Twitter have developed into rich sources for popular sentiment, providing insightful information about the attitudes, feelings, and reactions of the general public. In order to extract useful information from the enormous amount of content created by users on Twitter, sentiment analysis—the technique for autonomously recognising and classifying sentiment in textual data—is absolutely essential. In this article, we present a hybrid machine learning (ML)-based multilevel system for classifying Twitter sentiment. The Spiking neural network (SNN) is used in the suggested hybrid strategy (SNN+NB) suggested in this article in order to assist Naive Bayes (NB) classifiers make better decisions by giving NB an additional input. We employ a multilayer text mining application, involving data retrieval and query handling, to show the framework's functionality for language interpretation and sentiment analysis utilising hybrid machine learning. Performance metrics for our suggested SNN+NB method's accuracy, precision, recall, and F1-score are examined. The created framework can offer insightful information to people, organisations, and researchers looking to comprehend trends in public mood and make data-based choices using Twitter data.
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