This thesis examines the interplay of visual and textual discourse in Corporate Socia lResponsibility (CSR) reports, offering a systematic framework to analyse a dataset comprising around 66,925 images from 675 CSR reports. By analysing image attributes, colours, and objects in conjunction with textual sentiment and topics, we investigate the similarities, contrast and trends across various sectors and regions, and the impact of company characteristics. The mixed-methods approach, incorporating both qualitative image analysis and quantitative text evaluation, reveals patterns in how CSR initiatives are visually and textually communicated. Image and text extraction were accomplished using PyMuPDF and Tesseract libraries, harnessing the OCR capabilities. The identification of living objects was performed using OpenCV, while image classification was executed with the OpenAI-CLIP model, yielding high accuracy in extracting the visual content of the images. The developed framework achieved accuracy rate of 81% on living object identification using OpenCV model and 76% accuracy in object classification using OpenAI-CLIP model. The study's results indicate that the distinct patterns in how CSR is depicted, varying by sector, geographic location, and company size. These patterns offer key insights for developing more targeted and effective strategies for engaging with stakeholders.