AI Driven Sentiment Analysis Workflow for Customer Feedback

Sentiment Analysis for Customer Feedback and Reviews

1. Data Collection

1.1 Sources of Customer Feedback

• Online reviews (e.g., Google Reviews, Yelp)
• Social media platforms (e.g., Twitter, Facebook)
• Customer surveys and feedback forms
• Chatbot interactions on e-commerce sites

1.2 Tools for Data Collection

• Google Forms for surveys
• Scraping tools like Beautiful Soup for online reviews
• Social media monitoring tools (e.g., Hootsuite, Brandwatch)

2. Data Preprocessing

2.1 Cleaning the Data

• Removing duplicates and irrelevant content
• Standardizing text formats (e.g., lowercasing, removing punctuation)

2.2 Tools for Data Preprocessing

• Pandas library in Python for data manipulation
• NLTK or SpaCy for natural language processing tasks

3. Sentiment Analysis Implementation

3.1 Choosing an AI Model

• Pre-trained models (e.g., BERT, RoBERTa)
• Custom models using machine learning algorithms (e.g., SVM, Random Forest)

3.2 Tools for Sentiment Analysis

• Google Cloud Natural Language API
• AWS Comprehend
• IBM Watson Natural Language Understanding

4. Result Interpretation

4.1 Analyzing Sentiment Scores

• Classifying feedback as positive, negative, or neutral
• Identifying key themes and trends in customer sentiment

4.2 Visualization Tools

• Tableau for data visualization
• Power BI for interactive reports

5. Actionable Insights

5.1 Generating Reports

• Creating summaries of sentiment trends
• Highlighting areas for improvement based on customer feedback

5.2 Implementing Changes

• Adjusting product offerings based on customer preferences
• Enhancing customer service strategies to address negative feedback

6. Continuous Monitoring and Improvement

6.1 Setting Up Feedback Loops

• Regularly updating sentiment analysis models with new data
• Monitoring the impact of implemented changes on customer sentiment

6.2 Tools for Continuous Monitoring

• Real-time analytics dashboards (e.g., Google Data Studio)
• Social listening tools for ongoing feedback collection