| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| DM_JoãoBragança_MEI_2024 | 36.43 MB | Adobe PDF |
Authors
Advisor(s)
Abstract(s)
In 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 solu
Description
Keywords
Multimedia data analysis Object detection Speech recognition Sentiment analysis Content monitoring Logo detection Image Classification Optical Character Recognition