Teradata Package for R Function Reference | 17.00 - 17.00 - SentimentExtractor - Teradata Package for R

Description

The ExtractSentiment function extracts the sentiment (positive, negative, or neutral) of each input document or sentence, using either a classification model output by the TrainSentimentExtractor (td_sentiment_trainer_mle) function or a dictionary model.

Usage

  td_sentiment_extractor_mle (
      object = NULL,
      newdata = NULL,
      dict.data = NULL,
      text.column = NULL,
      language = "en",
      level = "DOCUMENT",
      high.priority = "NONE",
      filter = "ALL",
      accumulate = NULL,
      newdata.sequence.column = NULL,
      dict.data.sequence.column = NULL,
      newdata.order.column = NULL,
      dict.data.order.column = NULL
  )

Arguments

Value

Function returns an object of class "td_sentiment_extractor_mle" which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator using name: result.

Examples

    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("sentimenttrainer_example", "sentiment_train")
    loadExampleData("sentimentextractor_example", "sentiment_extract_input", "sentiment_word")
    
    # Create object(s) of class "tbl_teradata".
    sentiment_train <- tbl(con, "sentiment_train")
    sentiment_extract_input <- tbl(con, "sentiment_extract_input")
    sentiment_word <- tbl(con, "sentiment_word")
    
    # Example 1 - This example uses the dictionary model file and analysis level is document.
    td_sentiment_extractor_out1 <- td_sentiment_extractor_mle(object = "dictionary",
                                                         newdata = sentiment_extract_input,
                                                         text.column = "review",
                                                         level = "document",
                                                         accumulate = c("id","product")
                                                         )
    
    # Example 2 - This example uses the dictionary model file and analysis level is sentence.
    td_sentiment_extractor_out2 <- td_sentiment_extractor_mle(object = "dictionary",
                                                         newdata = sentiment_extract_input,
                                                         text.column = "review",
                                                         level = "sentence",
                                                         accumulate = c("id","product")
                                                         )
    
    # Example 3 - This example uses a maximum entropy classification model file.
    td_sentiment_extractor_out3 <- td_sentiment_extractor_mle(
                            object = "classification:default_sentiment_classification_model.bin",
                            newdata = sentiment_extract_input,
                            text.column = "review",
                            level = "document",
                            accumulate = c("id")
                            )
    
    # Example 4 - This example uses a model file output by the td_sentiment_trainer_mle() function.
    td_sentiment_trainer_out <- td_sentiment_trainer_mle(data = sentiment_train,
                                                     text.column = "review",
                                                     sentiment.column = "category",
                                                     model.file = "sentimentmodel1.bin"
                                                     )
                                                     
    td_sentiment_extractor_out4 <- td_sentiment_extractor_mle(
                                                    object = "classification:sentimentmodel1.bin",
                                                    newdata = sentiment_extract_input,
                                                    text.column = "review",
                                                    level = "document",
                                                    accumulate = c("id")
                                                    )
    
    # Example 5 - This example uses a dictionary instead of a model file.
    td_sentiment_extractor_out5 <- td_sentiment_extractor_mle(newdata = sentiment_extract_input,
                                                         dict.data = sentiment_word,
                                                         text.column = "review",
                                                         level = "document",
                                                         accumulate = c("id", "product")
                                                         )