Text-Based Gender Classification of Twitter Data using Naive Bayes and SVM Algorithm

被引：0
|
作者：
Angeles, Angelic [1 ]
Quintos, Maria Nikki [1 ]
Octaviano, Manolito, Jr. [1 ]
Raga, Rodolofo, Jr. [2 ]
机构：
[1] Natl Univ, Dept Comp Sci, Manila, Philippines
[2] Jose Rizal Univ, Dept Comp Sci, Mandaluyong, Philippines
来源：
2021 IEEE REGION 10 CONFERENCE (TENCON 2021) | 2021年
关键词：
machine learning; classification; gender; social media; Twitter; tweets; extraction; meta-attributes; Naive Bayes; Multinomial Naive Bayes; Support Vector Machine (SVM);
D O I：
10.1109/TENCON54134.2021.9707402
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents the development of the gender classification system on Twitter tweets. Three feature extraction techniques are explored: Bag of words and 2 variations of meta-attributes extraction. Feature sets are fed to Multinomial Naive Bayes and Support Vector Machine, and results were compared to see which algorithm can produce the best results in the classification task. Experiments show that the SVM outperformed the Naive Bayes algorithm, obtaining a performance of 56.31%.
引用
收藏
页码：522 / 526
页数：5
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