INTELLIGENT METHOD OF KNOWLEDGE EXTRACTION BASED ON SENTIMENT ANALYSIS

Abstract

The paper explores the impact of age and gender in sentiment analysis, as this data can help
e-commerce retailers increase sales by targeting specific demographic groups. The data set used
was created by collecting book reviews. A questionnaire was created containing questions about
preferences in books, as well as age groups and gender information. The article analyzes segmented
data on the subject of moods depending on each age group and gender. Sentiment analysis
was performed using various machine learning (ML) approaches, including maximum entropy,
support vector method, convolutional neural network, and long short-term memory. This paper
investigates the impact of age and gender in sentiment analysis, because this data can help
e-commerce retailers to increase sales by targeting specific demographic groups, as well as increase
the satisfaction of the needs of people of different age and gender groups. The dataset used
is generated by collecting book reviews. A questionnaire was created containing questions about
preferences in books (user opinions of e-books, paperbacks, hardbacks, images and audiobooks),
as well as data on age group and gender. In addition, the questionnaire also contains information
on a positive or negative opinion regarding preferences, which served as the basis for reliability
for the classifiers. As a result, 900 questionnaires were received, which were divided into groups
according to gender and age. Each specific group of data was divided into training and test one.
Segmented data were analyzed for sentiment analysis depending on age group and gender.
The age group “over 50 years old” showed the best results in comparison with all other age
groups in all classifiers; data in the female group performed higher accuracy compared to data from
the groups without gender information. The high scores shown by these groups indicate that sentiment
analysis approaches are able to predict moods in these groups better than in others. Sentiment
analysis was performed using a variety of machine learning (ML) approaches, including maximum
entropy, support vector machines, convolutional neural networks, and long short term memory.