Silva, Fábio André Souto daBragança, João Miguel Teixeira2024-12-172024-12-1720242024http://hdl.handle.net/10400.22/26891In today’s digital landscape, the rapid growth of multimedia content, particularly from influencers, has created a critical need for advanced monitoring tools. Building on previous research in multimedia data analysis, this dissertation proposes the development of a tool for extracting and analysing multimedia data to detect violations in influencer-produced content. The tool leverages pre-trained models such as Whisper.AI for speech recognition, YOLOv8 for object detection, and EasyOCR for Optical Character Recognition (OCR). Additionally, sentiment analysis models are employed and tested, with YOLOv8 further trained for specific tasks such as logo detection, ensuring adaptability to various use cases. The objective of this dissertation is to design a versatile and customisable tool capable of performing precise content analysis, including object detection, speech transcription, OCR, sentiment analysis, image classification and logo detection. The soluengMultimedia data analysisObject detectionSpeech recognitionSentiment analysisContent monitoringLogo detectionImage ClassificationOptical Character Recognitionmaster thesis203759940