Ensemble Classifier for Web Data Scraping with Lexicon Support

Abstract

An ensemble classifier for web data is used for selective web scraping with lexical support in an innovative way to improving the accuracy and efficiency of data classification from web sources. Web scraping, is a method for obtaining information from webpages, frequently produces massive, unstructured datasets and high risk in data reliability which leads to misuse in communication that are difficult to manage. To overcome this, selective online scraping is used to target certain information important to the classification task, resulting in less noise and higher data quality. The ensemble classifier integrates numerous machine learning models to maximize their strengths, resulting in better overall performance. In this approach, separate classifiers are trained on distinct subsets of scraped data that are chosen based on predetermined criteria utilizing lexicons, which are collections of domain-specific words and phrases. These lexicons guide the selective scraping process, ensuring that only the most relevant data is captured, hence improving classifier accuracy. After scraping and pre-processing the data, the ensemble method aggregates predictions from each classifier, generally using techniques like majority voting, stacking, or weighted average, to get a final classification result. This strategy not only promotes robustness by reducing the risk of overfitting, but it also improves flexibility across other domains by incorporating lexical assistance tailored to specific themes or sectors. The combination of selective web scraping and lexical assistance enables more targeted and resource-efficient data collecting, while the use of an ensemble classifier assures excellent accuracy and reliability in classification tasks. This methodology is especially useful in circumstances where the online data is large, dynamic, and contains a lot of unnecessary or noisy information. The resulting system provides a scalable and effective solution for real-time web data classification, with applications in sentiment analysis, content categorization, and market intelligence.