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
object |
Optional Argument. |
newdata |
Required Argument. |
newdata.order.column |
Optional Argument. |
dict.data |
Optional Argument. |
dict.data.order.column |
Optional Argument. |
text.column |
Required Argument. |
language |
Optional Argument. |
level |
Optional Argument. |
high.priority |
Optional Argument.
Default Value: "NONE" |
filter |
Optional Argument.
Default Value: "ALL" |
accumulate |
Optional Argument. |
newdata.sequence.column |
Optional Argument. |
dict.data.sequence.column |
Optional Argument. |
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")
)