Description
The SentimentEvaluator function uses test data to evaluate the precision and recall of the predictions output by the function SentimentExtractor.
Usage
td_sentiment_evaluator_mle ( object = NULL, obs.column = NULL, sentiment.column = NULL, object.sequence.column = NULL )
Arguments
object |
Required Argument. |
obs.column |
Required Argument. |
sentiment.column |
Required Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_sentiment_evaluator_mle" which is
a named list containing Teradata tbl object.
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 remote tibble objects. 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 'default_sentiment_lexicon.txt' td_sentiment_extractor_out1 <- td_sentiment_extractor_mle(object = "dictionary", newdata = sentiment_extract_input, text.column = "review", accumulate = c("category") ) td_sent_eval_out1 <- td_sentiment_evaluator_mle(object=td_sentiment_extractor_out1$result, obs.column='category', sentiment.column='out_polarity' ) # Example 2 - This example uses the classification model file # 'default_sentiment_classification_model.bin' td_sentiment_extractor_out2 <- td_sentiment_extractor_mle(object = "classification:default_sentiment_classification_model.bin", newdata = sentiment_extract_input, text.column = "review", accumulate = c("category") ) td_sent_eval_out2 <- td_sentiment_evaluator_mle(object=td_sentiment_extractor_out2$result, obs.column='category', sentiment.column='out_polarity' ) # Example 3 - This example uses classification model file output by # the SentimentTrainer function td_sentiment_trainer_out <- td_sentiment_trainer_mle(data = sentiment_train, text.column = "review", sentiment.column = "category", model.file = "sentimentmodel1.bin" ) # Use the sentiment extractor function to extract sentiment of each input document td_sentiment_extractor_out3 <- td_sentiment_extractor_mle(object = "classification:sentimentmodel1.bin", newdata = sentiment_extract_input, text.column = "review", accumulate = c("category") ) td_sent_eval_out3 <- td_sentiment_evaluator_mle(object=td_sentiment_extractor_out3$result, obs.column="category", sentiment.column="out_polarity" ) # Example 4 - This example uses a dictionary table ('sentiment_word') # instead of model file td_sentiment_extractor_out4 <- td_sentiment_extractor_mle(newdata = sentiment_extract_input, dict.data = sentiment_word, text.column = "review", accumulate = c("category") ) td_sent_eval_out4 <- td_sentiment_evaluator_mle(object=td_sentiment_extractor_out4$result, obs.column="category", sentiment.column="out_polarity" )