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.