Image & Sentiment Annotation

@glanegonsalves

Hi, Glane here. I can help with both the image annotation and sentiment labelling tasks while ensuring the IDs remain perfectly aligned across datasets for smooth downstream merging and analysis. I’m comfortable working with annotation platforms such as CVAT and LabelImg for accurate bounding-box creation and class tagging, with exports in COCO JSON or Pascal VOC format. For the social-media dataset, I can provide clean, well-structured CSV outputs containing post IDs and sentiment labels (positive, neutral, negative) with strong attention to consistency and quality control. I understand the importance of maintaining high annotation accuracy, avoiding mismatched or duplicate IDs, and following gold-standard labelling guidelines closely. I’m happy to begin with the test batch first, incorporate feedback quickly, and then continue with the remaining data in organised stages with clear communication throughout the project.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@fozansani05

Hi, Hope you are doing fine. I have experience with image labelling and annotation with high precision and currently doing projects. I can assure you quality as well as speed. Consider me for your project. Thanks & Regards, Fozan.

@smarketicsinc

High-quality training data that reaches 95%+ accuracy immediately, eliminating the need for manual cleanup or re-annotation. I will handle the end-to-end labeling process, ensuring strict ID consistency between your image bounding boxes and social media sentiment tags so your datasets merge perfectly without a single mismatched row. My execution plan is entirely done-for-you: 1. Validation of the gold-standard sample to align on labeling nuances. 2. Precise image annotation using CVAT, exporting in your preferred COCO JSON/Pascal VOC format. 3. Systematic sentiment tagging in CSV, mapped strictly to your unique IDs. 4. Final cross-verification audit to ensure 0% duplicate or missing IDs. You will have the first test batch delivered within 48 hours for your review to confirm the quality meets your threshold. I am deeply familiar with COCO/VOC standards and the precision required for professional ML pipelines. To resolve any concerns about data integrity, I use a verification script to validate ID alignment before final delivery. Send over the gold-standard sample and the test batch to begin.

@marialearrarte

You need careful and consistent labeling for two connected datasets: one for images requiring visual annotation and another for social media posts needing sentiment tagging. Here is exactly what I would build: - Precise bounding boxes around main subjects in images using tools like CVAT or LabelImg - Class names assigned to each bounding box - Sentiment labels (positive, neutral, negative) for social media posts in a CSV format - Ensure unique IDs are maintained for easy merging of datasets - A zipped export of the annotated image set in COCO JSON or Pascal VOC format - A CSV file listing each social media post ID alongside its sentiment label Timeline: 3 days. Price: 19330 INR. How will you provide the gold-standard sample for agreement assessment?

@glennw6

Hello, I’m interested in assisting with both the image annotation and sentiment labelling tasks. I have experience working with CVAT for bounding box, semantic segmentation, and skeleton annotation and can deliver clean, consistent exports in COCO JSON or Pascal VOC format. I’m comfortable handling class assignment, maintaining annotation accuracy, and following detailed labelling guidelines. For the sentiment dataset, I can accurately label posts as positive, neutral, or negative while preserving all existing IDs for seamless merging with the image dataset. I understand the importance of: * Maintaining consistent IDs across both datasets * Avoiding missing or duplicate entries * Delivering neatly structured outputs * Achieving high agreement with the provided gold-standard sample I’m comfortable working with staged batches, cloud-based workflows, and ongoing quality reviews. Clear communication and accuracy are priorities in my workflow. Deliverables will be provided exactly as requested: * Zipped annotated image export (COCO JSON or Pascal VOC) * CSV containing post IDs and sentiment labels I’m ready to begin with the test batch and can adapt quickly to your annotation standards. Best regards