AI Driven Sentiment Analysis Workflow for F&B Customer Reviews

Sentiment Analysis for Customer Reviews in F&B Industry

1. Data Collection

1.1 Identify Data Sources

Utilize platforms such as Yelp, Google Reviews, and TripAdvisor to gather customer reviews.

1.2 Data Extraction Tools

Implement web scraping tools like Scrapy or Beautiful Soup to automate the extraction of reviews.

2. Data Preprocessing

2.1 Text Cleaning

Remove irrelevant information such as HTML tags, special characters, and stop words using Natural Language Processing (NLP) libraries like NLTK or SpaCy.

2.2 Tokenization

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

3. Sentiment Analysis

3.1 AI Model Selection

Choose an appropriate sentiment analysis model, such as BERT or VADER, which are effective in understanding customer emotions.

3.2 Tool Integration

Utilize AI-driven tools like MonkeyLearn or Lexalytics to perform sentiment analysis on the preprocessed text.

4. Data Interpretation

4.1 Sentiment Scoring

Assign sentiment scores (positive, negative, neutral) to each review based on the analysis results.

4.2 Visualization

Employ data visualization tools like Tableau or Power BI to create dashboards that represent sentiment trends over time.

5. Reporting and Insights

5.1 Generate Reports

Compile findings into comprehensive reports that highlight key sentiment trends and customer feedback.

5.2 Actionable Insights

Provide recommendations for product improvements and marketing strategies based on sentiment analysis outcomes.

6. Continuous Improvement

6.1 Feedback Loop

Establish a feedback mechanism to continuously gather customer reviews and enhance the sentiment analysis process.

6.2 Tool Optimization

Regularly update and refine AI models and tools to adapt to changing customer sentiments and industry trends.