Year: 2025
Type: dissertation
Abstract: The purpose of this thesis was to develop an AI-powered system for automated tone analysis of website content for Kalibro.io, a digital optimization platform by Calevala Interactive Ltd. While Kalibro.io currently evaluates technical aspects like mobile friendliness, it does not yet analyse complex content tones. This process is not handled manually either. To address this gap, the research aimed to support the development of a new AI tool for Kalibro.io by extracting and segmenting text from website screenshots into meaningful chunks and classifying each segment into one of ten tone categories (Positive, Negative, Neutral, Formal, Informal, Confident, Friendly, Aggressive, Urgent, Informative). Key research questions included that How accurately can vision-language models extract text from website screenshots? And can fine-tuned NLP models reliably classify tones across diverse content types? The thesis adopted a practical, iterative development approach. First, text extraction was implemented using PaliGemma 2, a vision-language model, followed by segmentation via Open AI's GPT 4.1 to isolate logical units (e.g., headlines, paragraphs). For tone classification, RoBERTa was fine-tuned on a custom dataset labelled with the ten target tones. The system was tested on real-world websites, with performance evaluated using metrics like accuracy and F1-score, supplemented by qualitative case. This project introduces an innovative AI-powered tone analysis capability to Kalibro.io's platform, expanding its competitive advantage in digital optimization. By automatically evaluating content tone across ten distinct categories, the solution adds a new dimension to website assessment that complements existing technical metrics. The implementation enables Kalibro.io to offer clients unique insights into brand voice alignment and audience engagement - previously unavailable through conventional analysis tools. This enhancement positions the platform to deliver more comprehensive digital strategy services while maintaining its scalable, automated approach to website evaluation.
Source: Theseus (Ammattikorkeakoulujen)
Author: Rankiri Pathirage, Anuradha Priyankara
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7079911723