snowflake.snowpark.functions.ai_sentiment¶
- snowflake.snowpark.functions.ai_sentiment(text: Union[Column, str], categories: Optional[List[str]] = None) Column[source]¶
Returns overall and category sentiment in the given input text.
- Parameters:
text – A string containing the text in which sentiment is detected.
categories – An array containing up to ten categories (also called entities or aspects) for which sentiment should be extracted. Each category is a string. For example, if extracting sentiment from a restaurant review, you might specify
['cost', 'quality', 'service', 'wait time']as the categories. Each category may be a maximum of 30 characters long. If you do not provide this argument, AI_SENTIMENT returns only the overall sentiment.
- Returns:
name: The name of the category. The category names match the categories specified in thecategoriesargument.sentiment: The sentiment of the category. Each sentiment result is one of the following strings.unknown: The category was not mentioned in the text.positive: The category was mentioned positively in the text.negative: The category was mentioned negatively in the text.neutral: The category was mentioned in the text, but neither positively nor negatively.mixed: The category was mentioned both positively and negatively in the text.
The
overallcategory record is always included and contains the overall sentiment of the text.- Return type:
An OBJECT value containing a
categoriesfield.categoriesis an array of category records. Each category includes these fields
Note
AI_SENTIMENT can analyze sentiment in English, French, German, Hindi, Italian, Spanish, and Portuguese. You can specify categories in the language of the text or in English.
Examples:
>>> # Get overall sentiment only >>> session.range(1).select( ... ai_sentiment("A tourist's delight, in low urban light, Recommended gem, a pizza night sight. Swift arrival, a pleasure so right, Yet, pockets felt lighter, a slight pricey bite. 💰🍕🚀").alias("sentiment") ... ).show() ------------------------------ |"SENTIMENT" | ------------------------------ |{ | | "categories": [ | | { | | "name": "overall", | | "sentiment": "mixed" | | } | | ] | |} | ------------------------------ >>> # Extract sentiment for specific categories >>> df = session.create_dataframe([ ... ["The movie had amazing visual effects but the plot was terrible."], ... ["The food was delicious but the service was slow."], ... ["The movie was great, but the acting was terrible."] ... ], schema=["review"]) >>> df.select("review", ai_sentiment(col("review"), ['plot', 'visual effects', 'acting']).alias("sentiment")).show() ---------------------------------------------------------------------------------------- |"REVIEW" |"SENTIMENT" | ---------------------------------------------------------------------------------------- |The movie had amazing visual effects but the pl... |{ | | | "categories": [ | | | { | | | "name": "overall", | | | "sentiment": "mixed" | | | }, | | | { | | | "name": "acting", | | | "sentiment": "neutral" | | | }, | | | { | | | "name": "plot", | | | "sentiment": "negative" | | | }, | | | { | | | "name": "visual effects", | | | "sentiment": "positive" | | | } | | | ] | | |} | |The food was delicious but the service was slow. |{ | | | "categories": [ | | | { | | | "name": "overall", | | | "sentiment": "mixed" | | | }, | | | { | | | "name": "acting", | | | "sentiment": "unknown" | | | }, | | | { | | | "name": "plot", | | | "sentiment": "unknown" | | | }, | | | { | | | "name": "visual effects", | | | "sentiment": "unknown" | | | } | | | ] | | |} | |The movie was great, but the acting was terrible. |{ | | | "categories": [ | | | { | | | "name": "overall", | | | "sentiment": "mixed" | | | }, | | | { | | | "name": "acting", | | | "sentiment": "negative" | | | }, | | | { | | | "name": "plot", | | | "sentiment": "positive" | | | }, | | | { | | | "name": "visual effects", | | | "sentiment": "positive" | | | } | | | ] | | |} | ----------------------------------------------------------------------------------------