Teradata Package for R Function Reference | 17.00 - SentimentTrainer - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

TrainSentimentExtractor

Description

The TrainSentimentExtractor function trains a model, that is, takes training documents and outputs a maximum entropy classification model, which it installs on ML Engine. See Maximum Entropy for more information.

Usage

  td_sentiment_trainer_mle (
      data = NULL,
      text.column = NULL,
      sentiment.column = NULL,
      language = "en",
      model.file = NULL,
      data.sequence.column = NULL
  )

Arguments

data	Required Argument. Specifies the name of the tbl_teradata that contains the training data.
text.column	Required Argument. Specifies the name of the input tbl_teradata column that contains the training data. Types: character
sentiment.column	Required Argument. Specifies the name of the input tbl_teradata column that contains the sentiment values, which are "POS" (positive), "NEG" (negative), and "NEU" (neutral). Types: character
language	Optional Argument. Specifies the language of the training data: "en" (English), "zh_CN" (Simplified Chinese), "zh_TW" (Traditional Chinese). Default Value: "en" Permitted Values: en, zh_CN, zh_TW Types: character
model.file	Required Argument. Specifies the name of the file to which the function outputs the model. If a file of the same name already exists, it gets overwritten. Types: character
data.sequence.column	Optional Argument. Specifies the vector of column(s) that uniquely identifies each row of the input argument "data". The argument is used to ensure deterministic results for functions which produce results that vary from run to run. Types: character OR vector of Strings (character)

Value

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

Examples

  
    # Get the current context/connection
    con <- td_get_context()$connection
    
    # Load example data.
    loadExampleData("sentimenttrainer_example", "sentiment_train")
    
    # Create object(s) of class "tbl_teradata".
    # The sample dataset contains collection of user reviews for different products.
    sentiment_train <- tbl(con, "sentiment_train")
    
    # Example 1 - Build and output a maximum entropy classification model to a binary file.
    td_sentiment_trainer_mle_out <- td_sentiment_trainer_mle(data = sentiment_train,
                                                             text.column = "review",
                                                             sentiment.column = "category",
                                                             model.file = "sentimentmodel1.bin"
                                                             )