R: Lexicoder Sentiment Dictionary (2015)

data_dictionary_LSD2015 {quanteda} | R Documentation

## Lexicoder Sentiment Dictionary (2015)

### Description

The 2015 Lexicoder Sentiment Dictionary in quanteda dictionary format.

### Usage

    data_dictionary_LSD2015

### Format

A dictionary of four keys containing glob-style pattern matches.

`negative`

  2,858 word patterns indicating negative sentiment

`positive`

  1,709 word patterns indicating positive sentiment

`neg_positive`

  1,721 word patterns indicating a positive word preceded by a negation (used to convey negative sentiment)

`neg_negative`

  2,860 word patterns indicating a negative word preceded by a negation (used to convey positive sentiment)

### Details

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 http://www.snsoroka.com/data-lexicoder/.

### License and Conditions

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.

### References

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 http://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.

### Examples

    # simple example
    txt <- "This aggressive policy will not win friends."

    tokens_lookup(tokens(txt), dictionary = data_dictionary_LSD2015, exclusive = FALSE)
    ## 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 from 1 document.
    ## text1 :
    ## [1] "This"   "NEGATIVE"   "policy"   "will"   "NEG_POSITIVE" "POSITIVE."

    # on larger examples - notice that few negations are used
    dfm(data_char_ukimmig2010[1:5], dictionary = data_dictionary_LSD2015)

    # 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)

[Package quanteda version 3.1.0 Index]