AI-enhanced personality identification of websites
Supplementary material
Other Title
Authors
Chishti, Shafquat Ali
Ardekani, Iman
Varastehpour, Soheil
Ardekani, Iman
Varastehpour, Soheil
Author ORCID Profiles (clickable)
Degree
Grantor
Date
2024-10-10
Supervisors
Type
Journal Article
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
website personalities
websites
evaluation
Website Personality Scale
automated evaluation
websites
evaluation
Website Personality Scale
automated evaluation
ANZSRC Field of Research Code (2020)
Citation
Chishti, S. A., Ardekani, I., & Varastehpour, S. (2024). AI-enhanced personality identification of websites. Information, Special Issue in Recent Developments and Implications in Web Analysis, 15(10), 623(1-15). https://doi.org/10.3390/info15100623
Abstract
This paper addresses the challenge of objectively determining a website’s personality by developing a methodology based on automated quantitative analysis, thus avoiding the biases inherent in human surveys. Utilizing a database of 3000 websites, data extraction tools gather relevant data, which are then analyzed using Artificial Intelligence (AI) techniques, including machine learning (ML) and natural language processing. Four ML algorithms—K-means, Expectation Maximization, Hierarchical Agglomerative Clustering, and DBSCAN—are implemented to assess and classify website personality traits. Each algorithm’s strengths and weaknesses are evaluated in terms of data organization, cluster flexibility, and handling of outliers. A software tool is developed to facilitate the research process, from database creation and data extraction to ML application and results analysis. Experimental validation, conducted with identical training and testing datasets, achieves a success rate of up to 94% (with an Error of ≤50% ) in accurately identifying website personality, which is validated by subsequent surveys. The research highlights significant relationships between website attributes and personality traits, offering practical applications for website developers. For instance, developers can use these insights to design websites that align with business goals, enhance customer engagement, and foster brand loyalty. Additionally, the methodology can be applied to creating culturally resonant websites, thus supporting New Zealand’s cultural initiatives and promoting cross-cultural understanding. This research lays the groundwork for future studies and has broad applicability across various domains, demonstrating the potential for automated, unbiased website personality classification.
Publisher
MDPI (Multidisciplinary Digital Publishing Institute)
Permanent link
Link to ePress publication
DOI
https://doi.org/10.3390/info15100623
Copyright holder
Authors
Copyright notice
CC BY Attribution 4.0 International