Gender Classification of Social Network Text Using Natural Language Processing and Machine Learning Approaches
Supanat Jintawatsakoon, Ekkapob Poonsawat · 2023
Gender is a crucial consideration in many fields of study. In the era of social networks, massive volumes of data are gathered and processed, enabling us to use this data for a variety of purposes. Our objective to build a gender classification model based on Thai text using natural language processing (NLP) and a machine learning approach. We collected the data on social media websites using web scraping. TF-IDF and n-gram were applied for feature extraction tasks. Logistic Regression, Naïve Bayes, and Random Forest have implemented classification models. Accuracy, precision, recall, and f1 score are used as evaluation metrics and demonstrate that the Logistic Regression model trained on the features derived from data received from texts longer than 200 words produces the best outcome. The dataset is available at https://github.com/supanat/gender-classification-thai-text.git.