getSentiment {edgar} R Documentation

## Provides sentiment measures of EDGAR filings

### Description

getSentiment computes sentiment measures of EDGAR filings

### Usage

getSentiment(cik.no, form.type, filing.year, useragent)


### Arguments

 cik.no vector of CIK number of firms in integer format. Suppress leading zeroes from CIKs. Keep cik.no = 'ALL' if needs to download for all CIKs. form.type character vector containing form type to be downloaded. form.type = 'ALL' if need to download all forms. filing.year vector of four digit numeric year useragent Should be in the form of "Your Name Contact@domain.com"

### Details

getSentiment function takes CIK(s), form type(s), and year(s) as input parameters. The function first imports available downloaded filings in the local working directory 'Edgar filings_full text' created by getFilings function; otherwise, it automatically downloads the filings which are not already been downloaded. It then reads, cleans, and computes sentiment measures for these filings. The function returns a dataframe with filing information and sentiment measures. According to SEC EDGAR's guidelines a user also needs to declare user agent.

### Value

Function returns dataframe containing CIK number, company name, date of filing, accession number, and various sentiment measures. This function takes the help of Loughran-McDonald (L&M) sentiment dictionaries (https://sraf.nd.edu/loughranmcdonald-master-dictionary/) to compute sentiment measures of a EDGAR filing. Following are the definitions of the text characteristics and the sentiment measures:

file.size = The filing size of a complete filing on the EDGAR server in kilobyte (KB).

word.count = The total number of words in a filing text, excluding HTML tags and exhibits text.

unique.word.count = The total number of unique words in a filing text, excluding HTML tags and exhibits text.

stopword.count = The total number of stop words in a filing text, excluding exhibits text.

char.count = The total number of characters in a filing text, excluding HTML tags and exhibits text.

complex.word.count = The total number of complex words in the filing text. When vowels (a, e, i, o, u) occur more than three times in a word, then that word is identified as a complex word.

lm.dictionary.count = The number of words in the filing text that occur in the Loughran-McDonald (LM) master dictionary.

lm.negative.count = The number of LM financial-negative words in the document.

lm.positive.count = The number of LM financial-positive words in the document.

lm.strong.modal.count = The number of LM financial-strong modal words in the document.

lm.moderate.modal.count = The number of LM financial-moderate Modal words in the document.

lm.weak.modal.count = The number of LM financial-weak modal words in the document.

lm.uncertainty.count = The number of LM financial-uncertainty words in the document.

lm.litigious.count = The number of LM financial-litigious words in the document.

hv.negative.count = The number of words in the document that occur in the 'Harvard General Inquirer' Negative word list, as defined by LM.

### Examples

## Not run:

senti.df <- getSentiment(cik.no = c('1000180', '38079'),
form.type = '10-K', filing.year = 2006, useragent)

## Returns dataframe with sentiment measures of firms with CIKs
1000180 and 38079 filed in year 2006 for form type '10-K'.

senti.df <- getSentiment(cik.no = '38079', form.type = c('10-K', '10-Q'),
filing.year = c(2005, 2006), useragent)

## End(Not run)


[Package edgar version 2.0.5 Index]