QIAN-ME/bird_schema_data huggingface dataset:
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    List down app that does not have negative sentiment and give their average rating? doest not have negative sentiment refers to Sentiment! = 'Negative'; average = AVG(Sentiment_Polarity);

    【Evidence】
    doest not have negative sentiment refers to Sentiment! = 'Negative'; average = AVG(Sentiment_Polarity);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.App, AVG(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment != 'Negative' GROUP BY T1.App
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    List down application that have not been updated since 2015. What is the percentage of this application having more negative sentiment than positive sentiment? percentage = DIVIDE(SUBTRACT(SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), (SUM(Sentiment = 'Negative')) as percent; Last Updated>'2015';

    【Evidence】
    percentage = DIVIDE(SUBTRACT(SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), (SUM(Sentiment = 'Negative')) as percent; Last Updated>'2015';

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST((( SELECT COUNT(*) Po FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' AND T2.Sentiment = 'Positive' ) - ( SELECT COUNT(*) Ne FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' AND T2.Sentiment = 'Negative' )) AS REAL) * 100 / ( SELECT COUNT(*) NUM FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE SUBSTR(T1."Last Updated", -4, 4) > '2015' )
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    What is the percentage for free application with a rating 4.5 and above have not been updated since 2018? paid refers to Type = 'Paid'; application refers to App; Rating>4.5; Last Updated>'2018; percentage = DIVIDE(SUM(Genres = 'Mature 17+' and Rating>4.5 and substr("Last Updated",-4,4)>'2018' )), (COUNT(App)) as percent;

    【Evidence】
    paid refers to Type = 'Paid'; application refers to App; Rating>4.5; Last Updated>'2018; percentage = DIVIDE(SUM(Genres = 'Mature 17+' and Rating>4.5 and substr("Last Updated",-4,4)>'2018' )), (COUNT(App)) as percent;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(CASE WHEN SUBSTR('Last Updated', -4) > '2018' THEN 1 ELSE 0 END) AS REAL) * 100 / COUNT(App) PER FROM playstore WHERE Type = 'Free' AND Rating >= 4.5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    What genre does Honkai Impact 3rd belong to? Honkai Impact 3rd is the App;

    【Evidence】
    Honkai Impact 3rd is the App;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT Genres FROM playstore WHERE App = 'Honkai Impact 3rd'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    List down the rating for the App Learn C++. FALSE;

    【Evidence】
    FALSE;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT Rating FROM playstore WHERE App = 'Learn C++'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    What is the average price of games belonging in the arcade genre which has a content rating of Everyone 10+? average price = AVG(Price);

    【Evidence】
    average price = AVG(Price);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(Price) FROM playstore WHERE 'Content Rating' = 'Everyone 10+' AND Genres = 'Arcade'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    How much is the size of Browser 4G and how many users have a pretty positive favorability on it? Browser 4G is the App; pretty positive favorability refers to Sentiment_Polarity score = 0.5

    【Evidence】
    Browser 4G is the App; pretty positive favorability refers to Sentiment_Polarity score = 0.5

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.Size, COUNT(T1.App) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Browser 4G' AND T2.Sentiment_Polarity >= 0.5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    Name the Apps with a sentiment objectivity of 0.3 and include their number of installs. FALSE;

    【Evidence】
    FALSE;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT DISTINCT T1.App, T1.Installs FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment_Polarity = 0.3
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    How much is the average sentiment polarity score of Golf GPS Rangefinder: Golf Pad and what is it's rating in the Google Play Store? average sentiment polarity score = AVG(Sentiment_Polarity); Golf GPS Rangefinder: Golf Pad  is the App;

    【Evidence】
    average sentiment polarity score = AVG(Sentiment_Polarity); Golf GPS Rangefinder: Golf Pad  is the App;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT AVG(T2.Sentiment_Polarity), T1.Rating FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Golf GPS Rangefinder: Golf Pad'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    List the top 5 lowest rated puzzle games and count the number of negative sentiments the games received. lowest rating refers to MIN(Rating); puzzle is the genre;

    【Evidence】
    lowest rating refers to MIN(Rating); puzzle is the genre;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.App, COUNT(T1.App) COUNTNUMBER FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T2.Sentiment = 'Negative' GROUP BY T1.App ORDER BY T1.Rating LIMIT 5
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    What is the percentage ratio between positive sentiments and negative sentiments that are in Fate/Grand Order? Also indicate the current version. Fate/Grand Order is the App; percentage ratio = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), 100);

    【Evidence】
    Fate/Grand Order is the App; percentage ratio = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (SUM(Sentiment = 'Negative'))), 100);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT CAST(SUM(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE 0 END) AS REAL) * 100 / SUM(CASE WHEN T2.Sentiment = 'Negative' THEN 1 ELSE 0 END), T1.`Current Ver` FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Fate/Grand Order (English)' AND T1.`Current Ver` = '1.18.0'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    Indicate the number of installs and include the percentage of positive sentiments of FREEDOME VPN Unlimited anonymous Wifi Security. FREEDOME VPN Unlimited anonymous Wifi Security is the App; percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*))), 100)

    【Evidence】
    FREEDOME VPN Unlimited anonymous Wifi Security is the App; percentage = MULTIPLY(DIVIDE((SUM(Sentiment = 'Positive')), (COUNT(*))), 100)

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.Installs , CAST(SUM(CASE WHEN T2.Sentiment = 'Positive' THEN 1 ELSE 0 END) * 100 / SUM(CASE WHEN T2.Sentiment IS NOT NULL THEN 1.0 ELSE 0 END) AS REAL) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'FREEDOME VPN Unlimited anonymous Wifi Security'
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    For the Honkai Impact 3rd App, what is the highest sentiment polarity score and what genre does it belong to? highest sentiment polarity score refers to MAX(Sentiment_Polarity);

    【Evidence】
    highest sentiment polarity score refers to MAX(Sentiment_Polarity);

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT MAX(T2.Sentiment_Polarity), T1.Genres FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Honkai Impact 3rd' AND T2.Sentiment_Polarity > 0.5 GROUP BY T1.Genres
You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 app_store
【Schema】
# Table: user_reviews
[
(App:TEXT, Examples: [10 Best Foods for You]),
(Translated_Review:TEXT),
(Sentiment:TEXT, Examples: [Positive, nan, Neutral]),
(Sentiment_Polarity:TEXT, Examples: [1.0, 0.25, nan]),
(Sentiment_Subjectivity:TEXT, Examples: [0.5333333333333333, 0.28846153846153844, nan])
]
# Table: playstore
[
(App:TEXT, Examples: [Photo Editor & Candy Camera & Grid & ScrapBook]),
(Category:TEXT, Examples: [ART_AND_DESIGN, AUTO_AND_VEHICLES, BEAUTY]),
(Rating:REAL, Examples: [4.1, 3.9, 4.7]),
(Reviews:INTEGER, Examples: [159, 967, 87510]),
(Size:TEXT, Examples: [19M, 14M, 8.7M]),
(Installs:TEXT, Examples: [10,000+, 500,000+, 5,000,000+]),
(Type:TEXT, Examples: [Free, Paid, NaN]),
(Price:TEXT, Examples: [0, $4.99, $3.99]),
(Content Rating:TEXT, Examples: [Everyone, Teen, Everyone 10+]),
(Genres:TEXT, Examples: [Art & Design])
]
【Foreign keys】
user_reviews.App=playstore.App

    【Question】
    What is the rating of Dragon Ball Legends and how many users dislike this App? Dragon Ball Legends is the app; users who dislikes the app refers to Sentiment_Polarity<-0.5;

    【Evidence】
    Dragon Ball Legends is the app; users who dislikes the app refers to Sentiment_Polarity<-0.5;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT T1.Rating, COUNT(T2.Sentiment_Polarity) FROM playstore AS T1 INNER JOIN user_reviews AS T2 ON T1.App = T2.App WHERE T1.App = 'Dragon Ball Legends' AND CAST(Sentiment_Polarity AS INTEGER) < -0.5