library(mall)
data("reviews")
llm_use("ollama", "llama3.2", seed = 100, .silent = TRUE)
llm_sentiment(reviews, review)
#> # A tibble: 3 × 2
#> review .sentiment
#> <chr> <chr>
#> 1 This has been the best TV I've ever used. Great screen, and sound. positive
#> 2 I regret buying this laptop. It is too slow and the keyboard is to… negative
#> 3 Not sure how to feel about my new washing machine. Great color, bu… neutral
# Use 'pred_name' to customize the new column's name
llm_sentiment(reviews, review, pred_name = "review_sentiment")
#> # A tibble: 3 × 2
#> review review_sentiment
#> <chr> <chr>
#> 1 This has been the best TV I've ever used. Great screen, and … positive
#> 2 I regret buying this laptop. It is too slow and the keyboard… negative
#> 3 Not sure how to feel about my new washing machine. Great col… neutral
# Pass custom sentiment options
llm_sentiment(reviews, review, c("positive", "negative"))
#> # A tibble: 3 × 2
#> review .sentiment
#> <chr> <chr>
#> 1 This has been the best TV I've ever used. Great screen, and sound. positive
#> 2 I regret buying this laptop. It is too slow and the keyboard is to… negative
#> 3 Not sure how to feel about my new washing machine. Great color, bu… negative
# Specify values to return per sentiment
llm_sentiment(reviews, review, c("positive" ~ 1, "negative" ~ 0))
#> # A tibble: 3 × 2
#> review .sentiment
#> <chr> <dbl>
#> 1 This has been the best TV I've ever used. Great screen, and sound. 1
#> 2 I regret buying this laptop. It is too slow and the keyboard is to… 0
#> 3 Not sure how to feel about my new washing machine. Great color, bu… 0
# For character vectors, instead of a data frame, use this function
llm_vec_sentiment(c("I am happy", "I am sad"))
#> [1] "positive" "negative"
# To preview the first call that will be made to the downstream R function
llm_vec_sentiment(c("I am happy", "I am sad"), preview = TRUE)
#> ollamar::chat(messages = list(list(role = "user", content = "You are a helpful sentiment engine. Return only one of the following answers: positive, negative, neutral. No capitalization. No explanations. The answer is based on the following text:\nI am happy")),
#> output = "text", model = "llama3.2", seed = 100)Sentiment analysis
llm_sentiment
Description
Use a Large Language Model (LLM) to perform sentiment analysis from the provided text
Usage
llm_sentiment(
.data,
col,
options = c("positive", "negative", "neutral"),
pred_name = ".sentiment",
additional_prompt = ""
)
llm_vec_sentiment(
x,
options = c("positive", "negative", "neutral"),
additional_prompt = "",
preview = FALSE
) Arguments
| Arguments | Description |
|---|---|
| .data | A data.frame or tbl object that contains the text to be analyzed |
| col | The name of the field to analyze, supports tidy-eval |
| options | A vector with the options that the LLM should use to assign a sentiment to the text. Defaults to: ‘positive’, ‘negative’, ‘neutral’ |
| pred_name | A character vector with the name of the new column where the prediction will be placed |
| additional_prompt | Inserts this text into the prompt sent to the LLM |
| x | A vector that contains the text to be analyzed |
| preview | It returns the R call that would have been used to run the prediction. It only returns the first record in x. Defaults to FALSE Applies to vector function only. |
Value
llm_sentiment returns a data.frame or tbl object. llm_vec_sentiment returns a vector that is the same length as x.