Vignette taken directly from Christensen & Kenett (under review)

The SemNetDictionaries package contains several functions that facilitate the management and loading of dictionaries into SemNetCleaner. These dictionaries are used to spell-check and auto-correct the raw verbal fluency data in the preprocessing stage. In addition, this package includes a function that allows the user to create their own custom dictionaries, enabling them to save their own dictionaries for future use.

Pre-defined dictionaries

The dictionaries contained in the package currently include verbal fluency categories of animals, fruits, vegetables, and jobs as well as synonym categories of hot and good (Table 1). A general dictionary also accommodates phonological fluency tasks with the use of single letters (e.g., words that start with `f'). For each category and synonym dictionary, there is an accompanying moniker glossary that is used to automatically convert monikers (e.g., bear cat) and common misspellings (e.g., binterong) into a homogeneous response (e.g., binturong). These categories were included because the authors have data for them; however, we note that there are other possible verbal fluency categories.

Table 1. Dictionaries in SemNetDictionaries
Dictionary Entries Type
animals 1252 Category
fruits 488 Category
general 369327 All-purpose
good 284 Synonym
hot 281 Synonym
jobs 1471 Category
vegetables 284 Category

The development of these category and synonym dictionaries included searching for lists of category exemplars and for synonyms. Their development was also aided by responses from several hundred participants, who generated verbal fluency responses for some of these categories (Christensen, Kenett, Cotter, Beaty, & Silvia, 2018), as well as responses from other unpublished data. Finally, the general dictionary was added for phonological fluency tasks and generic spell-checking purposes. This dictionary was retrieved from the dwyl Github repository for English words: . To load some of these dictionaries, the following code can be used:

# Check for available dictionaries
# Load 'animals' dictionary
# Load all words starting with 'f'
# Load multiple dictionaries
load.dictionaries("fruits", "vegetables")

The load.dictionaries function will load as many dictionaries as are entered. The function will alphabetically sort and remove any duplicates found between the dictionaries. Thus, it returns an alphabetized vector the length of the unique words in the dictionaries.

Custom dictionaries

A notable feature of the SemNetDictionaries package is that users can define their own dictionaries using the append.dictionary function. With this function, users can create their own custom dictionaries or append pre-defined dictionaries so that they can be used for future data cleaning and preprocessing. To create your own dictionary, the following code can be used:

# Create a custom dictionary
append.dictionary("your", "words", "here",
                  "in", "quotations", "and",
                  "separated", "by", "commas",
         = "example",
                  save.location = "choose")

All words that are entered in quotations and separated by commas will be input into a new custom dictionary. The name of the dictionary can be defined with the argument (e.g., = "example"). All dictionaries that are saved using this function have a specific file suffix (*.dictionary.rds), which allows the dictionaries to be found by the function find.dictionaries(). A user can also append a pre-defined dictionary (e.g., animals.dictionary) by including the dictionary name in the function:

# Append a pre-defined dictionary
                  "tasselled wobbegong",
         = "new.animals",
                  save.location = "choose")

Appending a pre-defined dictionary does not overwrite it; instead, the user must save this dictionary somewhere on their computer (e.g., save.location = "choose"). Dictionaries from the SemNetDictionaries package and dictionaries stored using the append.dictionary function can be integrated into the SemNetCleaner package for preprocessing (we describe how this is done in the next section).


Christensen, A. P., & Kenett, Y. N. (under review). Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks. PsyArXiv.
Christensen, A. P., Kenett, Y. N., Cotter, K. N., Beaty, R. E., & Silvia, P. J. (2018). Remotely close associations: Openness to experience and semantic memory structure. European Journal of Personality, 32, 480–492.