Description
Abstract
This project leverages UiPath to automate sentiment analysis by integrating RPA with NLP techniques. It automates processes such as data extraction, preprocessing, and sentiment classification (positive, negative, or neutral) using AI/ML models. The solution streamlines customer feedback analysis, providing actionable insights to enhance decision-making and operational efficiency.
Problem Statement
Businesses often struggle to process and analyze vast amounts of unstructured text data like customer reviews, emails, or survey responses. Manual analysis is time-consuming, error-prone, and limits scalability. This project addresses these challenges by automating sentiment analysis to quickly and accurately gauge customer sentiment.
Solution Description
Tools and Technologies:
UiPath Components:
UiPath Studio for workflow development.
UiPath Orchestrator for deployment and scheduling.
UiPath Robots for execution.
NLP and AI/ML Models:
Pretrained models for sentiment analysis (e.g., Logistic Regression, SVM, or Random Forest).
Optional: Use APIs like Google NLP, Microsoft Azure, or custom ML models in Python.
Steps in the Process:
Data Collection:
Automate the extraction of text data from multiple sources such as emails, Excel files, or web scraping.
Preprocessing:
Clean and preprocess the data using string manipulation and regular expressions.
Sentiment Classification:
Integrate AI/ML models to classify sentiment as positive, negative, or neutral.
Trigger APIs or Python scripts for model predictions using UiPath activities.
Output Generation:
Generate reports in Excel or dashboards showcasing analyzed sentiments.
Benefits:
Efficiency: Automates repetitive tasks, saving time.
Accuracy: Leverages ML models for precise sentiment detection.
Scalability: Handles large datasets with minimal manual intervention.
Actionable Insights: Provides clear sentiment reports for better business decisions.
Key Results
Automated sentiment classification of 1,000+ customer reviews in under 10 minutes.
Delivered insights with an 85% accuracy using the Random Forest model.
Reduced manual workload by 70%, freeing up resources for strategic tasks.
Link
https://drive.google.com/drive/folders/1k0NSTe22TMLtyrwDgKZMHETjPtURw1A8?usp=drive_link
Date
2024-11-20