AI Powered Sentiment Analysis Workflow for Customer Feedback

Sentiment Analysis from Transcribed Customer Feedback

1. Data Collection

1.1 Gather Customer Feedback

Collect customer feedback through various channels such as surveys, social media, and customer service interactions.

1.2 Transcription of Feedback

Utilize AI transcription tools to convert audio or video feedback into text format. Examples include:

• Otter.ai
• Rev.com
• Google Speech-to-Text

2. Data Preparation

2.1 Clean and Preprocess Data

Remove any irrelevant information, correct transcription errors, and standardize the text for analysis.

2.2 Tokenization

Break down the text into individual words or phrases for more granular analysis.

3. Sentiment Analysis

3.1 Implement Sentiment Analysis Tools

Use AI-driven sentiment analysis tools to evaluate the emotional tone of the feedback. Recommended tools include:

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

3.2 Analyze Sentiment Scores

Interpret sentiment scores to categorize feedback into positive, negative, or neutral sentiments.

4. Data Visualization

4.1 Create Visual Reports

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

• Tableau
• Power BI
• Google Data Studio

4.2 Share Insights with Stakeholders

Disseminate visual reports to relevant stakeholders for informed decision-making.

5. Continuous Improvement

5.1 Monitor Feedback Trends

Regularly track sentiment trends over time to identify areas for improvement and customer satisfaction.

5.2 Iterate on Feedback Collection Methods

Refine feedback collection and analysis processes based on insights gained to enhance future customer interactions.