Gender Prediction from Social Media Comments with Artificial Intelligence

Year 2019, , 1256 - 1264, 01.12.2019

Özer Çelik , Ahmet Faruk Aslan

https://doi.org/10.16984/saufenbilder.559452

Cited By: 7

Abstract

In the 21st century,
which can be termed as artificial age of intelligence, machine learning
techniques that can become widespread and improve themselves can be given more
quality services to humanity in many fields. As a result of these developments,
nowadays many companies deliver their products and services to their customers
via social media accounts. But not every customer is interested in all product
or service. Each customer's area of interest is different. Gender is one of the
main reasons for this difference. If the gender of a social media user is
determined correctly, the amount of sales may be increased by offering the
appropriate products or services. The main aim of our study is an estimation of
genders of the commenters thanks to machine learning techniques by analyzing
the comments of companies posting on Facebook. As a result of the study the
genders of the commenters were labelled according to the names by collecting
the comments from Facebook. The data set is divided into training and test data
as 70-30%. As a result of the study, it was seen that machine learning methods
predicted with similar accuracy rates, while the highest accuracy rate (74.13%)
was obtained by logistic regression method.

Keywords

Gender Prediction, Artificial Intelligence, Machine Learning, Natural Language Processing, Sentiment Analysis

Gender Prediction from Social Media Comments with Artificial Intelligence

Abstract

In the 21st century, which can be termed as artificial age of intelligence, machine learning techniques that can become widespread and improve themselves can be given more quality services to humanity in many fields. As a result of these developments, nowadays many companies deliver their products and services to their customers via social media accounts. But not every customer is interested in all product or service. Each customer's area of interest is different. Gender is one of the main reasons for this difference. If the gender of a social media user is determined correctly, the amount of sales may be increased by offering the appropriate products or services. The main aim of our study is an estimation of genders of the commenters thanks to machine learning techniques by analyzing the comments of companies posting on Facebook. As a result of the study the genders of the commenters were labelled according to the names by collecting the comments from Facebook. The data set is divided into training and test data as 70-30%. As a result of the study, it was seen that machine learning methods predicted with similar accuracy rates, while the highest accuracy rate (74.13%) was obtained by logistic regression method.

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