AI Driven Sentiment Analysis Workflow for Customer Feedback

Sentiment Analysis for Customer Feedback Processing

1. Data Collection

1.1 Source Identification

Identify sources of customer feedback, including:

• Online surveys
• Social media platforms
• Travel agency websites
• Customer service interactions

1.2 Data Aggregation

Utilize AI tools to aggregate data from identified sources. Examples include:

• MonkeyLearn: For extracting feedback from various channels.
• Zapier: To automate data collection from different platforms.

2. Data Preprocessing

2.1 Cleaning Data

Implement natural language processing (NLP) techniques to clean the data by removing:

• Stop words
• Punctuation
• Irrelevant information

2.2 Tokenization

Break down the feedback into individual words or phrases for analysis.

3. Sentiment Analysis

3.1 Model Selection

Choose an appropriate AI model for sentiment analysis, such as:

• Google Cloud Natural Language API: For comprehensive sentiment analysis.
• IBM Watson: For advanced sentiment and emotion detection.

3.2 Training the Model

Train the selected model using historical customer feedback data to improve accuracy.

3.3 Sentiment Scoring

Utilize the trained model to score customer feedback on a scale (e.g., positive, negative, neutral).

4. Insights Generation

4.1 Data Visualization

Employ visualization tools to present sentiment analysis results effectively. Examples include:

• Tableau: For creating interactive dashboards.
• Power BI: To visualize sentiment trends over time.

4.2 Reporting

Generate reports summarizing key insights and trends in customer sentiment.

5. Actionable Recommendations

5.1 Identifying Improvement Areas

Analyze sentiment insights to identify areas of improvement in services or products.

5.2 Implementing Changes

Collaborate with relevant departments to implement changes based on feedback.

6. Continuous Monitoring

6.1 Feedback Loop

Establish a continuous feedback loop to monitor customer sentiment over time.

6.2 Model Refinement

Regularly refine the sentiment analysis model with new data to maintain accuracy.