The 2015 Lexicoder Sentiment Dictionary in quanteda dictionary format.
data_dictionary_LSD2015A dictionary of four keys containing glob-style pattern matches.
negative2,858 word patterns indicating negative sentiment
positive1,709 word patterns indicating positive sentiment
neg_positive1,721 word patterns indicating a positive word preceded by a negation (used to convey negative sentiment)
neg_negative2,860 word patterns indicating a negative word preceded by a negation (used to convey positive sentiment)
The dictionary consists of 2,858 "negative" sentiment words and 1,709 "positive" sentiment words. A further set of 2,860 and 1,721 negations of negative and positive words, respectively, is also included. While many users will find the non-negation sentiment forms of the LSD adequate for sentiment analysis, Young and Soroka (2012) did find a small, but non-negligible increase in performance when accounting for negations. Users wishing to test this or include the negations are encouraged to subtract negated positive words from the count of positive words, and subtract the negated negative words from the negative count.
Young and Soroka (2012) also suggest the use of a pre-processing script to remove specific cases of some words (i.e., "good bye", or "nobody better", which should not be counted as positive). Pre-processing scripts are available at https://www.snsoroka.com/data-lexicoder/.
The LSD is available for non-commercial academic purposes only. By using
data_dictionary_LSD2015, you accept these terms.
Please cite the references below when using the dictionary.
The objectives, development and reliability of the dictionary are discussed in detail in Young and Soroka (2012). Please cite this article when using the Lexicoder Sentiment Dictionary and related resources. Young, L. & Soroka, S. (2012). Lexicoder Sentiment Dictionary. Available at https://www.snsoroka.com/data-lexicoder/.
Young, L. & Soroka, S. (2012). Affective News: The Automated Coding of Sentiment in Political Texts. doi:10.1080/10584609.2012.671234 . Political Communication, 29(2), 205--231.
# simple example
txt <- "This aggressive policy will not win friends."
tokens_lookup(tokens(txt), dictionary = data_dictionary_LSD2015, exclusive = FALSE)
#> Tokens consisting of 1 document.
#> text1 :
#> [1] "This" "NEGATIVE" "policy" "will" "NEG_POSITIVE"
#> [6] "POSITIVE" "POSITIVE" "."
#>
## tokens from 1 document.
## text1 :
## [1] "This" "NEGATIVE" "policy" "will" "NEG_POSITIVE" "POSITIVE" "POSITIVE" "."
# notice that double-counting of negated and non-negated terms is avoided
# when using nested_scope = "dictionary"
tokens_lookup(tokens(txt), dictionary = data_dictionary_LSD2015,
exclusive = FALSE, nested_scope = "dictionary")
#> Tokens consisting of 1 document.
#> text1 :
#> [1] "This" "NEGATIVE" "policy" "will" "NEG_POSITIVE"
#> [6] "POSITIVE" "."
#>
## tokens from 1 document.
## text1 :
## [1] "This" "NEGATIVE" "policy" "will" "NEG_POSITIVE" "POSITIVE."
# compound neg_negative and neg_positive tokens before creating a dfm object
toks <- tokens_compound(tokens(txt), data_dictionary_LSD2015)
dfm_lookup(dfm(toks), data_dictionary_LSD2015)
#> Document-feature matrix of: 1 document, 4 features (25.00% sparse) and 0 docvars.
#> features
#> docs negative positive neg_positive neg_negative
#> text1 1 1 1 0