Ecommerce Sentiment Analysis from Reviews

@HiraMahmood4072

Hello, With over 7 years of experience in Data Science, Statistical Analysis, Statistics, and Python, I have carefully read through your project requirements for Ecommerce Sentiment Analysis from Reviews. To address this project, I propose to first ingest the historical customer reviews and survey responses, then conduct sentiment analysis using tools like NLTK, spaCy, or transformers in Python. The approach will focus on analyzing sentiment patterns at the product, category, and global store levels. The deliverables will include cleaned, well-documented code, a visual and written summary of sentiment distribution, and a CSV/Excel file with sentiment scores appended to each original review/response. I am confident in achieving the required 80% precision and recall on the sample provided and ensuring the final report is easily understandable to non-technical stakeholders. I would appreciate the opportunity to discuss this project further in chat to provide a more detailed plan on how I intend to execute this sentiment analysis effectively. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@khaliduom103

Hi I am an experienced data analyst of sale data with the help of python Jupyter notebook.I will provide my past work so you will check the results there.

@KirtikaKataria

Hi there, I understand you need to turn your product-level customer reviews and open-ended survey responses into actionable insights through robust sentiment analysis, providing clarity on shopper perceptions at the product, category, and overall store level. My approach will begin by ingesting your historical reviews and survey responses, cleaning the text to remove noise, standardize formatting, and handle missing or inconsistent entries. Using Python (with libraries such as NLTK, spaCy, or transformers) or R (tidytext), I will build a defensible sentiment analysis pipeline that assigns positive, neutral, or negative scores to each entry. I will also identify key drivers of sentiment, highlighting trends, recurring praise or complaints, and temporal patterns for each product and category. Next, I will produce a CSV/Excel file with sentiment scores appended to the original reviews/responses, enabling you to track and filter insights easily. Alongside this, I will provide a visual and written report summarizing overall sentiment distributions, top factors influencing positive and negative feelings, and notable trends that can inform merchandising, messaging, and support strategies. All code or notebooks will be fully documented and rerunnable for future data. QUESTION: Do you want the analysis to also flag product-specific issues automatically for quicker operational action, or purely focus on sentiment trends? Let's chat and get started now! Regards, Shehwani.

@anirudhat

Turning your review and survey text into actionable sentiment intelligence is exactly the kind of NLP project I specialize in. I'll ingest both data sources, clean and normalize the text, then run sentiment classification using Python with a fine-tuned transformer model (likely a distilled BERT variant) benchmarked against your manually verified sample to hit the 80%+ precision and recall threshold. Beyond overall polarity scores, I'll extract aspect-level sentiment so you can see exactly which product attributes—quality, shipping, value—drive positive or negative emotion across product, category, and store levels. You'll receive a rerunnable Jupyter notebook, an executive-ready visual report highlighting key trends, and a scored CSV appended to every original review. I can start immediately and have built similar pipelines for ecommerce clients using NLTK, spaCy, and Hugging Face transformers.

@jesica46

Hello, I hope you’re having a great day. I reviewed your project and I would be happy to assist you with your Data Analysis needs. As a professional data analyst, my goal is to transform raw data into clear and meaningful insights that help clients understand their data and make better, data-driven decisions. I can help you clean and organize raw or unstructured data, perform accurate and detailed analysis, identify trends and patterns, and create professional charts, graphs, and dashboards. I will also provide a clear, well-structured report with actionable insights so that the results are easy to understand and useful for decision-making. I have experience working with tools such as Microsoft Excel, Google Sheets, Python, and Power BI, which allow me to analyze data efficiently and present the results in a professional and easy-to-understand format. I always focus on delivering high-quality and accurate work, maintaining clear communication with clients, ensuring fast and on-time delivery, and providing complete client satisfaction. I would love to learn more about your project. Could you please share the dataset and let me know what type of analysis or insights you are looking for? Once I review the details, I can start working immediately and deliver the results as quickly and accurately as possible. Thank you for your time and consideration. I look forward to working with you. Best regards,

@eliza019

Hello there, I hope you’re doing great! I’m a professional Python Developer with experience in developing efficient, clean, and reliable Python scripts and applications. Whether it’s data analysis, automation, web scraping, API integration, or backend development — I can handle it with precision and quality. I always focus on writing well-structured, optimized, and bug-free code. My goal is to deliver work that meets your requirements perfectly and adds value to your project. ✅ Clean and optimized Python code ✅ Fast delivery and regular updates ✅ Unlimited revisions until satisfaction ✅ Excellent communication I would love to discuss your project in detail and start right away. Let’s turn your ideas into powerful Python solutions! Best regards,

@jepita200

Hello there,, I have reviewed your requirements and I'm confident that my experience and skills align perfectly with what you're looking for. I'm confident my skills are perfect for the job! As an accomplished AI expert with a strong proficiency in python programming and data processing, I feel confident that I can complete your project brilliantly within the given timeline. My 10+ years of professional experience have equipped me with in-depth knowledge and expertise in various fields of artificial intelligence, especially computer vision, machine learning, deep learning, and image processing -- all of which could greatly benefit your project. Client interaction is another aspect where I strongly focus; together we can optimize not just the Python implementation but the project's overall progression too. Having said that, let's discuss this further and embark on a project journey that exceeds your expectations! Best Regrads.....

@mindmagic1

Hi, I can perform a detailed sentiment analysis of your product reviews and post-purchase survey responses, producing actionable insights at the product, category, and store-wide level. I have experience using Python (NLTK, spaCy, transformers) and R (tidytext) to generate reliable, interpretable sentiment models. First, I’ll clean and preprocess your data, ensuring consistency and removing noise. Next, I’ll build and validate a sentiment model targeting ≥80% precision and recall on your manually verified sample. Finally, I’ll deliver a report with overall sentiment distributions, key drivers of positive/negative feedback, and trends over time, accompanied by a CSV/Excel with sentiment scores appended to each original response. You’ll receive well-documented code/notebooks for reruns on future data, visual and written summaries understandable to non-technical stakeholders, and actionable insights to guide merchandising, messaging, and support improvements.

@JawadIT

Hi there, I am having 12+ years of experience in App Development, Architecture, GenAI, AgenticAi, RAG etc. I have understood your requirements and can do this analysis for you, even for your future classifications after this pilot run, i can build it as a product and we can use Python + OpenAI Codex CLI to generate this full fledge analysis tool for your which will involve robustness and can achieve about 90% of accuracy for sure. Let me know, if you are interested!

@Hemantt841

Hi, I see you want to extract actionable insights from your product reviews and post-purchase survey responses through sentiment analysis. You’re aiming to move beyond intuition by using data to shape merchandising and support strategies. Your project requires processing two data sources—historical customer reviews and structured survey answers—to deliver sentiment patterns at product, category, and global store levels. You also want clear methodology, a well-documented, reusable codebase, and a report digestible by non-technical stakeholders, with a precision and recall target of at least 80%. I recently completed a sentiment analysis project using Python’s transformers and spaCy libraries on e-commerce reviews, delivering a clean Jupyter notebook with sentiment scores appended to each review. I provided a visual and written summary highlighting key sentiment drivers and trends, ensuring the results were accessible to business teams. This approach aligns well with your requirements. I can complete this within 10 days after receiving your CSV exports. Let’s discuss the next steps to get started on turning your reviews into clear sentiment insights.

@ihsan191

Hi there, I am excited about the opportunity to work on analyzing the sentiment of your accumulated customer reviews and survey responses to provide valuable insights for your store. With my expertise in sentiment analysis and proficiency in tools like Python with NLTK and spaCy, I am confident in delivering accurate and actionable results. My approach will involve carefully analyzing the historical data, identifying sentiment patterns at different levels, and presenting the findings in a clear and understandable manner. I will ensure the model achieves the required precision and recall rates, and the final report will be tailored for non-technical stakeholders. I will provide a cleaned, well-documented code or notebook for future use, along with a visual and written summary highlighting sentiment distribution and key drivers of emotions. The CSV/Excel file with sentiment scores appended to each review will enable easy reference and decision-making. I am committed to meeting your project goals within the specified timeline and am eager to collaborate on future analytics projects related to purchase behavior and review frequency. Looking forward to the opportunity to work together, Ihsan Faridi

@Lazarone1

Hi, that’s great to hear! Your project closely aligns with one I recently completed. In that project, I built a full sentiment‑analysis pipeline for large‑volume ecommerce reviews using Python, spaCy, and transformer‑based models with automated text cleaning, model evaluation, and product‑level reporting. For your dataset of historical reviews and survey responses, I can deliver a clear, defensible methodology, visual summaries of sentiment trends across products and categories, and an easy‑to‑digest report built for non‑technical stakeholders. You’ll also receive a rerunnable notebook and sentiment‑scored CSVs that meet your 80% precision and recall requirement. I’d be glad to connect and share my experience in more detail over chat. Thank you. Best regards, Lazar

@yogeshjoshi9

Hi, I can help analyze your customer reviews and survey responses to generate clear sentiment insights for products, categories, and overall brand experience. I’ll use a reliable Python NLP approach (NLTK, spaCy, or transformer models) to clean the data, run sentiment analysis, and ensure the model meets the 80% precision and recall requirement using your verified sample. You’ll receive a well-documented notebook/code, a CSV/Excel file with sentiment scores for each review, and a simple visual report highlighting sentiment distribution, key positive/negative drivers, and trends by product or timeframe. The results will be easy for non-technical stakeholders to understand and reuse for future data. I can complete the project within the two-week timeline and would be happy to support future analytics work as well.

@rashidaakterpro

Hello, I am Rashidab and I can start right now. I can turn your reviews and survey responses into a clear sentiment analysis report that is accurate, easy to understand, and reusable on future data. I will deliver cleaned documented code, a stakeholder friendly summary with visuals and insights by product category and time period, plus a file with sentiment scores added to each record. I can follow a defensible Python workflow and validate performance against your sample to meet the required standard.

@peterj514

Hi there,\nYour backlog of customer reviews indicates a wealth of untapped insights. I've spent the last 4 years focusing on sentiment analysis, specifically transforming raw text data into actionable insights. I will ingest your historical customer reviews and post-purchase survey responses, applying Python libraries like NLTK or spaCy to ensure a robust analysis. The final deliverables will include clean, well-documented code for future use, a summary report detailing sentiment distribution, and a CSV with sentiment scores attached to each review. \n\nBest regards,

@zeldac1

Hi there, I understand you want a defensible, data-driven sentiment analysis pipeline that clarifies how shoppers feel about specific items, categories, and your store experience, with results you can act on, not just numbers. My Plan: - Ingest and harmonize historical reviews and survey responses. - Build a transparent sentiment model (lexicon + ML, with explainable outputs) and test rigorously to meet 80% precision/recall on your sample. - Produce per-item, per-category, and store-level sentiment dashboards with clear drivers of positive/negative sentiment. - Deliver well-documented code/notebook that reruns on new data and a CSV with sentiment scores aligned to each original record. - Provide a concise written summary highlighting trends over time and actionable merchandising/support recommendations. In a closely related engagement, I built a Python-based sentiment analysis workflow for a consumer brand where we automated data ingestion, produced interpretable sentiment scores, and delivered stakeholder-friendly visuals and a reproducible notebook, leading to a 15% improvement in targeted messaging and a 10% uplift in customer satisfaction scores. Best regards, Thando

@romanr008

Hello, As we progress into an increasingly data-driven world, understanding and leveraging sentiment analysis has become a critical component for businesses, especially in the ecommerce realm. With my core expertise lying in Machine Learning, Deep Learning, and, more notably, Natural Language Processing (NLP), I am well-poised to aid you with your project of extracting meaningful sentiments from your customer reviews and surveys. Having developed AI systems using Python-based libraries such as NLTK, spaCy and transformers - alongside R and tidytext -, delivering accurate results with easy-to-understand methodologies is second nature to me. My solutions have historically achieved high precision and recall rates; a criterion you require satisfactorily. I don't just offer my technical skills but also the ability to communicate technical concepts clearly. My aim is always to produce executable yet comprehensible code that can be helpful for your future data, which aligns perfectly with your project objective. Besides, if this initial project goes smoothly, I have the necessary skills for your future analytics projects. Ultimately, my focus remains on developing robust AI solutions that generate genuine business value to underpin your strategic decision-making processes. With me at the helm of this analysis, you're ensured an interpretive report that Thanks!

@benediktd

Hello, With my strong proficiency in Python, I am well-equipped to tackle your sentiment analysis project head-on. I have considerable experience leveraging libraries such as NLTK, spaCy, and transformers for insightful data-driven analytics. Given my diligent approach to project execution, you can be confident in receiving clean, well-documented code that will be easily applicable to future tasks. I understand the depth of your needs beyond just executing the task at hand. In addition to providing a comprehensive visual and written report of the sentiment distributions, my background in communication ensures that your non-technical stakeholders will fully grasp the findings. This ensures a smoothly operating process for all involved parties. Finally, my commitment extends beyond this one-time project; I envision an ongoing relationship where we can utilize not only sentiment analysis but also explore purchase behavior and review frequency. I invite you to leverage my skills to revolutionize your decision-making from hunches to hard data. Let's collaborate on making your brand strategies more precise and impactful. Thanks!

@dorofiip

Hello there What is the best way to handle nuances in sentiment from different product categories? How should we weigh recent survey responses versus older product reviews in the analysis? Sentiment analysis at the product level is challenging due to varied language and context across categories. Combining unstructured reviews with structured survey responses requires careful data harmonization. I will build a clear, documented sentiment model using Python libraries like NLTK or transformers for accurate and interpretable results. The output will include a detailed, easy-to-understand report and data files ready for future use. The timeline will fit your two-week window after receiving the raw CSV data. I would be happy to discuss further on chat.