You will get Sentiment Analysis Service for Customer Reviews

Project details
Get actionable insights from up to 1,000 customer reviews with a custom-built sentiment analysis app. My Flask-based solution delivers positive, negative, or neutral classifications with confidence scores, enhanced by spell correction (e.g., “horrble” to “horrible”) for accuracy. Enjoy a clean, artistic UI with elegant fonts, plus visualizations (pie chart, histogram, etc..) and CSV/PDF outputs. With flexible delivery revisions, I ensure a production-ready app with SQLite storage and rate limiting, perfect for small businesses. My expertise in Python, NLP, and web development guarantees a high-quality, user-friendly tool to elevate your decision-making.
Machine Learning Tools
pandas, Python, Python Scikit-Learn, PyTorch, SciPy, SQL, TensorFlowWhat's included
| Service Tiers |
Starter
$40
|
Standard
$50
|
Advanced
$60
|
|---|---|---|---|
| Delivery Time | 7 days | 10 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 3 | 6 |
Model Validation/Testing | - | - | |
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - | - | - |
Frequently asked questions
About Baraa
Information Engineering Bachelor | Data Analyst & AI Engineer
Duesseldorf, Germany - 4:46 am local time
Steps for completing your project
After purchasing the project, send requirements so Baraa can start the project.
Delivery time starts when Baraa receives requirements from you.
Baraa works on your project following the steps below.
Revisions may occur after the delivery date.
Collect and Validate Client Data
I’ll review the client’s dataset (CSV or text, up to 1,000 reviews) to ensure it meets requirements (valid text, no malicious content). If needed, I’ll assist in formatting the data for compatibility with the app’s input system.
Perform Sentiment Analysis
Using the app’s model with spell correction, I’ll process the reviews to classify them as positive, negative, or neutral, with confidence scores. Misspellings (e.g., “horrble” to “horrible”) will be corrected for accuracy.


