Open Access

ARTICLE

Author’s Age and Gender Prediction on Hotel Review Using Machine Learning Techniques

by Muhammad Hood Khan¹, Bilal Khan^1,*, Saifullah Jan¹, Muhammad Imran Chughtai²

1 Department of Computer Science, City University of Science and Information Technology, Peshawar, 25000, Pakistan
2 Department of Computer Science, Sarhad University of Science and Information Technology, Mardan Campus, Mardan, Pakistan

* Corresponding Author: Bilal Khan. Email:

Journal on Big Data 2023, 5, 41-56. https://doi.org/10.32604/jbd.2022.044060

Received 20 July 2023; Accepted 14 September 2023; Issue published 17 November 2023

Abstract

Author’s Profile (AP) may only be displayed as an article, similar to text collection of material, and must differentiate between gender, age, education, occupation, local language, and relative personality traits. In several information-related fields, including security, forensics, and marketing, and medicine, AP prediction is a significant issue. For instance, it is important to comprehend who wrote the harassing communication. In essence, from a marketing perspective, businesses will get to know one another through examining items and websites on the internet. Accordingly, they will direct their efforts towards a certain gender or age restriction based on the kind of individuals who comment on their products. Recently many approaches have been presented many techniques to automatically detect user age and gender from the language which is based on text, documents, or comments on social media. The purpose of this research is to classify age (18–24, 25–34, 35–49, 50–64, and 65–70) and gender (male, female) from a PAN 2014 Hotel Reviews dataset of the English language. The usage of six machine learning models is the main emphasis of this work, including the methods of Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Logistic Regression (LR), Decision Tree (DT) and K-Nearest Neighbors (KNN).

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Keywords

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