Text Classification Using Genetic Programming with Implementation of Map Reduce and Scraping

International Journal on Informatics Visualization: JOIV

article

Wirarama Wedashwara¹  Budi Irmawati¹  Heri Wijayanto¹  I Wayan Agus Arimbawa²  Vandha Pradwiyasma Widartha³ 

[1] University of Mataram;Seoul National University;Telkom University

关键词: Text Classification;    Genetic Programming;    Web Scraping;    Map-reduce;   

DOI  :  10.30630/joiv.7.2.1813

来源: Politeknik Negeri Padang

【 摘 要 】

Classification of text documents on online media is a big data problem and requires automation. Text classification accuracy can decrease if there are many ambiguous terms between classes. Hadoop Map Reduce is a parallel processing framework for big data that has been widely used for text processing on big data. The study presented text classification using genetic programming by pre-processing text using Hadoop map-reduce and collecting data using web scraping. Genetic programming is used to perform association rule mining (ARM) before text classification to analyze big data patterns. The data used are articles from science-direct with the three keywords. This study aims to perform text classification with ARM-based data pattern analysis and data collection system through web-scraping, pre-processing using map-reduce, and text classification using genetic programming. Through web scraping, data has been collected by reducing duplicates as much as 17718. Map-reduce has tokenized and stopped-word removal with 36639 terms with 5189 unique terms and 31450 common terms. Evaluation of ARM with different amounts of multi-tree data can produce more and longer rules and better support. The multi-tree also produces more specific rules and better ARM performance than a single tree. Text classification evaluation shows that a single tree produces better accuracy (0.7042) than a decision tree (0.6892), and the lowest is a multi-tree(0.6754). The evaluation also shows that the ARM results are not in line with the classification results, where a multi-tree shows the best result (0.3904) from the decision tree (0.3588), and the lowest is a single tree (0.356).

【 授权许可】

Unknown