Arabic Hotel Review Annotation

@sareheshak

عليكم‎ السلام,,, AVAILABLE TO START IMMEDIATELY..,,. I will precisely annotate your Arabic hotel reviews for aspect-level sentiment (positive/negative/neutral) and deliver the fully structured dataset in Excel, adhering strictly to your guidelines. 10+ years Advanced Excel experience, Certified VBA Programmer, MBA.

@Deenaibraheem

Hi there This is Deena a native arabic professional translator and annotation work. Have the pleasure to work with you. Regards

@EightVeer

Hi there, We will annotate your Arabic hotel reviews at aspect level in Excel, labeling mentions such as cleanliness, location, and service with positive, negative, or neutral sentiment according to your guideline. We will keep the structure consistent, handle edge cases carefully, and return a clean, review-ready file. We have public Freelancer review history covering strategic review, structured business research and supplier-sourcing engagements. Would you like us to follow the sample exactly, or apply the guideline wherever it resolves edge cases? The proposed initial deliverable is campaign and market diagnostic with prioritised recommendations for Arabic Hotel Review Annotation; any subsequent implementation would require a separate Freelancer milestone. Best Regards, 8veer

@ImranAli01

Hi, I got that you are looking for precise sentiment annotation for Arabic hotel customer reviews, focusing on aspects like cleanliness and location. This is what I can help you with, let's chat. My approach is to utilize advanced sentiment analysis techniques to accurately label each feature with its corresponding sentiment in the dataset. By leveraging state-of-the-art tools and methods, I will ensure that the final annotated dataset provides fine-grained insights for your downstream models, enhancing the overall analysis quality. As final deliverables, you will receive a fully annotated dataset in Excel format, following the provided guideline document. One thing I'd like to confirm before we start: Could you please provide more details about the specific categories of hotel aspects you would like to focus on for sentiment annotation? Looking forward to discussing this project further with you. Regards, Imran

@MZITSERVICES

As a highly skilled data scientist with experience in both natural language processing and Excel, I am equipped to effectively deliver precise, aspect-level sentiment annotation for your Arabic hotel review dataset. My extensive technical skills, including proficiency in Python and working knowledge of Arabic, make me especially well-suited for your project. Having worked with sentiment analysis and data manipulation before, I understand the subtleties of customer reviews and can ensure that every mention of aspects such as cleanliness and location is accurately labeled with their corresponding sentiment. I believe I am the perfect candidate for this task as I'll not only provide sentiment annotations but will also leverage my experience to offer additional insights into your data. Let's discuss the project further; I'm eager to bring my expertise to your team! Thanks Arm

@Nuel24

ARABIC REVIEW ANNOTATION IS REALLY ABOUT CONSISTENCY, NOT JUST UNDERSTANDING THE LANGUAGE. A SMALL INCONSISTENCY IN HOW ASPECTS OR SENTIMENTS ARE labelled can quickly affect the quality of the dataset and the model trained on it. I’d approach this by first validating the guideline against the provided sample, then annotating each relevant hotel aspect with positive, negative, or neutral sentiment while keeping the same interpretation throughout the dataset. I’ll also pay close attention to context, especially where the sentiment is implied rather than explicitly stated, and deliver the completed annotations cleanly in Excel. Before starting the full dataset, could you share the sample and annotation guideline so I can confirm the expected labelling approach? Nuel

@alluankit01

Hi, I can annotate your Arabic hotel review dataset with precise aspect-level sentiment labels for categories like cleanliness, location, service, room quality, food, price, facilities, and other hotel-related aspects. The best solution is to first review your annotation guideline and sample reviews, then complete a small test batch to confirm label consistency. After that, I’ll annotate each aspect mention with positive, negative, or neutral sentiment, following the guideline exactly and keeping the final Excel file clean and easy to review. I’m comfortable with Arabic text annotation, customer review analysis, aspect-based sentiment labeling, NLP dataset preparation, Excel formatting, quality checking, and consistent guideline-based data labeling. Deliverables will include: * Annotated Arabic hotel reviews * Aspect-level sentiment labels * Positive/negative/neutral classification * Excel final dataset * Guideline-based annotation * Sample batch confirmation * Consistency check * Unclear cases flagged separately * Final review before delivery I’ll focus on accurate, consistent, and model-ready annotations so your downstream sentiment analysis system can learn detailed hotel customer feedback patterns. Best regards Ankit

@Andyloredo

I will deliver the fully annotated corpus of arabic hotel reviews with precise aspect-level sentiment annotation. The first execution step involves carefully reviewing the guideline document to ensure complete understanding of the required aspect categories for cleanliness, location, and other hotel features. Following this, I will process the provided arabic customer reviews to apply the sentiment labels (positive, negative, or neutral) to each relevant mention, creating the required annotation file. The final deliverable will be an Excel file containing the original text alongside the precise aspect annotations for each review. I will check the quality by manually verifying a sample set against the provided guidelines before final delivery. To ensure accurate annotation, could you confirm if the guideline document specifies handling of mixed sentiments within a single review?

@panos1414

Hello, I can handle this Arabic hotel review annotation work with attention to aspect-level detail and consistent sentiment labeling. I’m comfortable working from a guideline document, following category definitions carefully, and delivering clean Excel output ready for downstream use. Since you’ll provide a sample, I’ll use it to align precisely with your annotation rules before processing the full corpus. My focus will be on accuracy, consistency, and preserving the distinctions between positive, negative, and neutral mentions for each hotel aspect. Best regards, Panagiotis

@mostafaharira

As an experienced freelancer, I have developed a deep understanding of sentiment analysis and its application in various contexts. While my previous projects might not have been directly centered around Arabic hotel reviews, my expertise has trained me to quickly adapt to new domains and deliver high-quality work. My proficiency in Arabic language combined with my data-driven approach to problem-solving makes me the ideal candidate for your project. It is worth noting that I understand the importance of providing well-detailed and correctly labeled datasets. From your description, I gather that you require an annotation that goes beyond just marking sentiments as positive or negative. I assure you that my annotations would be precise and will provide fine-grained insights, enhancing the capabilities of your downstream models to extract maximum value from the dataset. Additionally, my commitment to quality is paralleled with a passion for clear communication. We can have open discussions and iterate on the output until it perfectly aligns with your expectations. Finally, rest assured of timely delivery of the final annotated dataset in readily usable Excel format. With my diverse skill set and track record for exceptional results, I am confident we will produce outstanding work together.

@Hanken8or

We've recently helped a client achieve highly accurate sentiment analysis for their customer feedback data. I will help you create a fully annotated dataset for your Arabic hotel reviews, focusing on aspect-level sentiment like cleanliness and location. I understand the importance of delivering a clean, user-friendly, and professional dataset, formatted as specified in your guidelines. Our expertise in sentiment analysis ensures that we can provide precise labels for each feature, although we are still building our portfolio on freelancer.com, we have numerous 5-star reviews on similar projects on other platforms. It would be our privilege to assist you with this project, and choosing us is a decision you won't regret. There's no pressure at all. Best case, we create something great together. Worst case, you get a free consultation and a clearer direction for your project. Regards, Henco Burger.

@shainw

Are you struggling to gain insights from your corpus of Arabic hotel-related customer reviews? I can help you achieve precise, aspect-level sentiment annotation that will enhance your downstream models' performance. I specialize in sentiment analysis and am experienced in annotating datasets with a focus on features such as cleanliness, location, and more. My approach ensures that each mention is accurately labeled as positive, negative, or neutral, adhering to best practices for maximum effectiveness. You can expect clear communication, organized milestones, and regular progress updates throughout the project. I’ll review your guidelines and provide the fully annotated dataset in Excel format, ensuring it meets your specific needs. Drop me a message, and I'll share examples of my previous work in sentiment annotation. Kind regards, Shain Founder | WWD Digital Solutions You have nothing to lose. If we're not the right fit, you'll still receive a FREE professional consultation to help point your project in the right direction—no obligation. ?

@yehyaashour11223

Arabic is my first language, so the dialect mixed into MSA in hotel reviews won't be a problem, that is usually where aspect-level annotation goes wrong. How I'd work it: I follow your guideline document exactly, one row per aspect mention, review id, aspect, polarity, and the exact span the label came from, so every label is traceable back to its text and the file drops straight into training. Consistency across the corpus matters more than any single label, so anything genuinely ambiguous (negation, sarcasm, two sentiments in one sentence) goes into an edge-case log and gets flagged rather than guessed. We settle those once instead of forty times. Send me the sample now and I'll annotate it and send it back before you award anything, you can judge the format and the judgment calls before you commit to me. One question so my timeline is honest: roughly how many reviews are in the corpus? My bid assumes up to about 2,000 aspect mentions; more is fine, I'd just tell you the real number of days.