This example uses the td_sentiment_extractor_mle() sentiment extraction function from the tdplyr package to evaluate and classify a set of restaurant reviews.
The input data table "restaurant_reviews" table is listed here.
|1||This restaurant was great. The food was amazing. Our waiter was excellent. The appetizers in particular were very creative and well-thought-out.|
|2||I really enjoyed my meal, and my daughter's steak was perfectly prepared. The chocolate torte was superb.|
|3||The service was terrible! The food was ok, but the bread was stale and the drinks were very weak.|
|4||Not a must-do. Perfectly adequate but nothing special for the price.|
|5||I can't recommend this place. Service was slow and unfriendly. Food so-so.|
|6||Definitely a good choice for a special occasion. Highly recommended!|
Create a tibble from the input data table "restaurant_reviews".
tddf_restaurant_reviews <- tbl(con, "restaurant_reviews")This example uses the default values for many arguments of the td_sentiment_extractor_mle() function. One of those is the object argument which specifies the source used to assign sentiment values to words. The default value of object argument is a built-in dictionary based on the WordNet lexical database.
Call the sentiment extraction function.
td_sentiment_extractor_out <- td_sentiment_extractor_mle( object = "dictionary", newdata = tddf_restaurant_reviews, level = "document", text.column = "review_text", accumulate = c("id") )
Inspect the result.