AI_SENTIMENT

AI_SENTIMENT(text)

SELECT 
    'I love this product!' AS text,
    AI_SENTIMENT('I love this product!') AS

SELECT 
    review_id,
    review_text,
    AI_SENTIMENT(review_text) AS sentiment,
    CASE 
        WHEN AI_SENTIMENT(review_text) > 0.5 THEN 'Positive'
        WHEN AI_SENTIMENT(review_text) < -0.5 THEN 'Negative'
        ELSE 'Neutral'
    END AS sentiment_category
FROM

SELECT 
    DATE_TRUNC('week', created_date) AS week,
    AVG(AI_SENTIMENT(review_text)) AS avg_sentiment,
    COUNT(*) AS review_count
FROM product_reviews
WHERE created_date >= CURRENT_DATE - 90
GROUP BY week
ORDER BY

SELECT 
    product_id,
    product_name,
    AVG(AI_SENTIMENT(review_text)) AS avg_sentiment,
    COUNT(CASE WHEN AI_SENTIMENT(review_text) > 0.5 THEN 1 END) AS positive_reviews,
    COUNT(CASE WHEN AI_SENTIMENT(review_text) < -0.5 THEN 1 END) AS negative_reviews
FROM reviews
GROUP BY product_id, product_name
ORDER BY avg_sentiment DESC

SELECT 
    ticket_id,
    customer_message,
    AI_SENTIMENT(customer_message) AS sentiment_score
FROM support_tickets
WHERE AI_SENTIMENT(customer_message) < -0.3
  AND status = 'Open'
ORDER BY sentiment_score ASC

-- Create meaningful categories
SELECT 
    CASE 
        WHEN AI_SENTIMENT(text) >= 0.6 THEN 'Very Positive'
        WHEN AI_SENTIMENT(text) >= 0.2 THEN 'Positive'
        WHEN AI_SENTIMENT(text) >= -0.2 THEN 'Neutral'
        WHEN AI_SENTIMENT(text) >= -0.6 THEN 'Negative'
        ELSE 'Very Negative'
    END AS sentiment_category,
    COUNT(*) AS count
FROM feedback
GROUP BY

SELECT 
    product_id,
    AVG(rating) AS avg_rating,
    AVG(AI_SENTIMENT(review_text)) AS avg_sentiment,
    CORR(rating, AI_SENTIMENT(review_text)) AS rating_sentiment_correlation
FROM reviews
GROUP BY

CREATE VIEW sentiment_trends AS
SELECT 
    DATE_TRUNC('day', created_at) AS date,
    product_category,
    AVG(AI_SENTIMENT(feedback_text)) AS avg_sentiment,
    STDDEV(AI_SENTIMENT(feedback_text)) AS sentiment_volatility
FROM customer_feedback
GROUP BY date,

WITH daily_sentiment AS (
    SELECT 
        DATE(created_at) AS date,
        AVG(AI_SENTIMENT(comment)) AS avg_sentiment
    FROM social_media_mentions
    WHERE brand = 'OurBrand'
    GROUP BY date
)
SELECT *
FROM daily_sentiment
WHERE avg_sentiment < -0.3
  AND date >= CURRENT_DATE - 7
ORDER BY date DESC

SELECT 
    ticket_id,
    customer_email,
    AI_SENTIMENT(ticket_description) AS sentiment,
    CASE 
        WHEN AI_SENTIMENT(ticket_description) < -0.6 THEN 'High Priority'
        WHEN AI_SENTIMENT(ticket_description) < -0.2 THEN 'Medium Priority'
        ELSE 'Normal Priority'
    END AS priority
FROM support_tickets
WHERE status = 'Open'

SELECT 
    launch_date,
    product_name,
    AVG(AI_SENTIMENT(social_mention)) AS avg_sentiment,
    COUNT(*) AS mention_count
FROM social_media_monitoring
WHERE product_name = 'New Product X'
  AND mention_date BETWEEN launch_date AND launch_date + 30
GROUP BY launch_date,