Check for sentiment dictionaries — get_dictionary • experienceAnalysis

On a server, the sentiment dictionaries have to be manually uploaded. On a local machine they can be loaded using {tidytext}. Check to see if they are already loaded (by a previous function run), if they are on disk (which would indicate the code is on a server), or just load using {tidytext}.

get_dictionary(dictionary)

Arguments

dictionary	A string. One of "afinn" (Nielsen, 2013), "nrc" (Mohammad & Turney, 2013) or "bing" (Hu & Liu, 2004), indicating the dictionary to be loaded.

Value

A data frame with two columns: the word and its sentiment according to the requested sentiment dictionary.

References

Hu M. & Liu B. (2004). Mining and summarizing customer reviews. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004), Seattle, Washington, USA, Aug 22-25, 2004.

Mohammad S.M. & Turney P.D. (2013). Crowdsourcing a Word–Emotion Association Lexicon. Computational Intelligence, 29(3):436-465.

Nielsen F.A. (2013). A new ANEW: Evaluation of a word list for sentiment analysis in microblogs. Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big things come in small packages 718 in CEUR Workshop Proceedings 93-98. https://arxiv.org/abs/1103.2903.

Silge J. & Robinson D. (2017). Text Mining with R: A Tidy Approach. Sebastopol, CA: O’Reilly Media. ISBN 978-1-491-98165-8.

Examples

    get_dictionary("afinn")
#> # A tibble: 2,477 x 2
#>    word       value
#>    <chr>      <dbl>
#>  1 abandon       -2
#>  2 abandoned     -2
#>  3 abandons      -2
#>  4 abducted      -2
#>  5 abduction     -2
#>  6 abductions    -2
#>  7 abhor         -3
#>  8 abhorred      -3
#>  9 abhorrent     -3
#> 10 abhors        -3
#> # ... with 2,467 more rows
    get_dictionary("nrc")
#> # A tibble: 13,901 x 2
#>    word        sentiment
#>    <chr>       <chr>    
#>  1 abacus      trust    
#>  2 abandon     fear     
#>  3 abandon     negative 
#>  4 abandon     sadness  
#>  5 abandoned   anger    
#>  6 abandoned   fear     
#>  7 abandoned   negative 
#>  8 abandoned   sadness  
#>  9 abandonment anger    
#> 10 abandonment fear     
#> # ... with 13,891 more rows
    get_dictionary("bing")
#> # A tibble: 6,786 x 2
#>    word        sentiment
#>    <chr>       <chr>    
#>  1 2-faces     negative 
#>  2 abnormal    negative 
#>  3 abolish     negative 
#>  4 abominable  negative 
#>  5 abominably  negative 
#>  6 abominate   negative 
#>  7 abomination negative 
#>  8 abort       negative 
#>  9 aborted     negative 
#> 10 aborts      negative 
#> # ... with 6,776 more rows