AI Driven Sentiment Analysis for Citizen Feedback Workflow

Sentiment Analysis for Citizen Feedback

1. Data Collection

1.1 Identify Feedback Sources

Collect citizen feedback from various channels including:

• Social media platforms (e.g., Twitter, Facebook)
• Online surveys and forms
• Government websites and portals
• Email correspondence

1.2 Gather Data

Utilize web scraping tools and APIs to aggregate feedback data.

• Example Tool: Beautiful Soup for web scraping
• Example Tool: Google Forms API for survey data

2. Data Preprocessing

2.1 Clean Data

Remove duplicates, irrelevant content, and non-English text.

2.2 Text Normalization

Apply techniques such as:

• Lemmatization and stemming
• Removing stop words
• Lowercasing text

3. Sentiment Analysis

3.1 Select AI Tools

Choose appropriate AI-driven tools for sentiment analysis:

• Example Tool: IBM Watson Natural Language Understanding
• Example Tool: Google Cloud Natural Language API
• Example Tool: Microsoft Azure Text Analytics

3.2 Implement Sentiment Analysis

Utilize selected tools to analyze the sentiment of the collected feedback.

• Classify feedback as positive, negative, or neutral.
• Generate sentiment scores and visualizations.

4. Reporting and Insights

4.1 Generate Reports

Create comprehensive reports detailing sentiment trends over time.

• Use data visualization tools like Tableau or Power BI to present findings.

4.2 Provide Actionable Insights

Identify key areas for improvement based on sentiment analysis results.

• Highlight specific services or issues that require attention.
• Recommend policy changes or enhancements to services.

5. Continuous Improvement

5.1 Monitor Feedback

Establish a continuous feedback loop by regularly collecting and analyzing new citizen feedback.

5.2 Update AI Models

Periodically retrain AI models to improve accuracy and adapt to changing citizen sentiments.

5.3 Stakeholder Engagement

Engage with stakeholders to discuss findings and implement changes based on insights.