Edit

Share via

Detect sentiment with the ai.analyze_sentiment function

The ai.analyze_sentiment function uses Generative AI to detect whether the emotional state expressed by input text is positive, negative, mixed, or neutral—all with a single line of code. If the function can't determine the sentiment, it leaves the output blank.

AI functions turbocharge data engineering by putting the power of Fabric's built-in large languages models into your hands. To learn more, visit this overview article.

Important

This feature is in preview, for use in the Fabric 1.3 runtime and higher.

  • Review the prerequisites in this overview article, including the library installations that are temporarily required to use AI functions.
  • By default, AI functions are currently powered by the gpt-3.5-turbo (0125) model. To learn more about billing and consumption rates, visit this article.
  • Although the underlying model can handle several languages, most of the AI functions are optimized for use on English-language texts.
  • During the initial rollout of AI functions, users are temporarily limited to 1,000 requests per minute with Fabric's built-in AI endpoint.

Use ai.analyze_sentiment with pandas

The ai.analyze_sentiment function extends the pandas Series class. Call the function on pandas DataFrame text column to detect the sentiment of each input row.

The function returns a pandas Series that contains sentiment labels, which can be stored in a new column of the DataFrame.

Syntax

df["sentiment"] = df["text"].ai.analyze_sentiment()

Parameters

None

Returns

The function returns a pandas Series that contains sentiment labels for each input text row. Each sentiment label is positive, negative, neutral, or mixed. If a sentiment can't be determined, the return value is null.

Example

# This code uses AI. Always review output for mistakes. 
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/

df = pd.DataFrame([
        "The cleaning spray permanently stained my beautiful kitchen counter. Never again!",
        "I used this sunscreen on my vacation to Florida, and I didn't get burned at all. Would recommend.",
        "I'm torn about this speaker system. The sound was high quality, though it didn't connect to my roommate's phone.",
        "The umbrella is OK, I guess."
    ], columns=["reviews"])

df["sentiment"] = df["reviews"].ai.analyze_sentiment()
display(df)

Use ai.analyze_sentiment with PySpark

The ai.analyze_sentiment function is also available for Spark DataFrames. The name of an existing input column must be specified as a parameter.

The function returns a new DataFrame, with sentiment labels for each input text row stored in an output column.

Syntax

df.ai.analyze_sentiment(input_col="text", output_col="sentiment")

Parameters

Name Description
input_col
Required		 A string that contains the name of an existing column with input text values to be analyzed for sentiment.
output_col
Optional		 A string that contains the name of a new column to store the sentiment label for each row of input text. If this parameter isn't set, a default name is generated for the output column.
error_col
Optional		 A string that contains the name of a new column to store any OpenAI errors that result from processing each row of input text. If this parameter isn't set, a default name is generated for the error column. If an input row has no errors, the value in this column is null.

Returns

A Spark DataFrame with a new column containing sentiment labels that match each row of text in the input column. Each sentiment label is positive, negative, neutral, or mixed. If a sentiment can't be determined, the return value is null.

Example

# This code uses AI. Always review output for mistakes. 
# Read terms: https://azure.microsoft.com/support/legal/preview-supplemental-terms/

df = spark.createDataFrame([
        ("The cleaning spray permanently stained my beautiful kitchen counter. Never again!",),
        ("I used this sunscreen on my vacation to Florida, and I didn't get burned at all. Would recommend.",),
        ("I'm torn about this speaker system. The sound was high quality, though it didn't connect to my roommate's phone.",),
        ("The umbrella is OK, I guess.",)
    ], ["reviews"])

sentiment = df.ai.analyze_sentiment(input_col="reviews", output_col="sentiment")
display(sentiment)

Related content